diff --git a/modules/dnn/CMakeLists.txt b/modules/dnn/CMakeLists.txt
new file mode 100644
index 000000000..f85139bea
--- /dev/null
+++ b/modules/dnn/CMakeLists.txt
@@ -0,0 +1,5 @@
+set(the_description "Deep neural netork module. Allow load models and make forward pass")
+set(OPENCV_MODULE_IS_PART_OF_WORLD OFF)
+ocv_define_module(dnn opencv_imgproc opencv_core opencv_highgui WRAP python)
+
+target_link_libraries(opencv_dnn)
diff --git a/modules/dnn/include/opencv2/dnn.hpp b/modules/dnn/include/opencv2/dnn.hpp
new file mode 100644
index 000000000..74e1fd753
--- /dev/null
+++ b/modules/dnn/include/opencv2/dnn.hpp
@@ -0,0 +1,171 @@
+#ifndef __OPENCV_DNN_HPP__
+#define __OPENCV_DNN_HPP__
+
+#include <opencv2/core.hpp>
+#include <map>
+#include <vector>
+
+namespace cv
+{
+namespace dnn
+{
+    class Layer;
+    class NetConfiguration;
+    class Net;
+    class Blob;
+    class LayerParams;
+
+    //wrapper over cv::Mat and cv::UMat
+    class Blob : public _InputOutputArray
+    {
+    public:
+        Blob(Mat &in);
+        Blob(const Mat &in);
+        Blob(UMat &in);
+        Blob(const UMat &in);
+        
+        int width() const; //cols
+        int height() const; //rows
+        int channels() const;
+        int num() const;
+
+        Vec4i size() const;
+    };
+
+    class LayerParams
+    {
+        struct Value
+        {
+            int type;
+            
+            union
+            {
+                int i;
+                double d;
+                String *s;
+                Mat *m;
+            } data;
+        };
+
+        //TODO: maybe this mechanism was realized somewhere in OpenCV?
+        std::map<String, Value> params;
+
+    public:
+
+        std::vector<Blob> learnedWeights;
+
+        template<typename T>
+        T get(const String &name);
+    };
+
+    //this class allows to build new Layers
+    class Layer : public Algorithm
+    {
+    public:
+        //Layer registration routines
+
+        typedef Layer* (*Constuctor)();
+
+        static void registerLayer(const String &type, Constuctor constructor);
+
+        static void unregisterLayer(const String &type);
+
+        static Ptr<Layer> createLayerInstance(const String &type);
+
+    public:
+
+        //TODO: this field must be declared as public if we want support possibility to change these params in runtime
+        std::vector<Blob> learnedParams;
+        
+        virtual ~Layer();
+
+        //type of Layer
+        virtual String type() const;
+
+        //setUp calls once (think that it's constructor)
+        virtual void setUp(LayerParams &params);
+
+        //after setUp the following two function must be able to return values
+        virtual int getNumInputs();
+        virtual int getNumOutputs();
+
+        //maybe useless function
+        //shape of output blobs must be adjusted with respect to shape of input blobs
+        virtual void adjustShape(const std::vector<Blob> &inputs, std::vector<Blob> &outputs);
+
+        virtual void forward(std::vector<Blob> &inputs, std::vector<Blob> &outputs);
+
+    private:
+        
+        static std::map<String, Constuctor> registeredLayers;
+    };
+
+    //TODO: divide NetConfiguration interface and implementation, hide internal data
+    //TODO: maybe eliminate all int ids and replace them by string names
+    //Proxy class for different formats
+    //Each format importer must populate it
+    class NetConfiguration
+    {
+    public:
+
+        static Ptr<NetConfiguration> create();
+
+        int addLayer(const String &name, const String &type);
+
+        void deleteLayer(int layerId);
+        
+        void setLayerParams(int layerId, LayerParams &params);
+
+        //each output of each layer can be labeled by unique string label (as in Caffe)
+        //if label not specified then %layer_name%:c_%N% will be used
+        void setLayerOutputLabels(int layerId, const std::vector<String> &outputNames);
+
+        //version #1
+        void addConnection(int fromLayer, int fromLayerOutput, int toLayer, int toLayerInput);
+
+        //or maybe version #2
+        inline int getBlobId(int layerId, int inputOutputNumber)
+        {
+            return layerId << 16 + inputOutputNumber;
+        }
+
+        void addConnection(int outputId, int inputId);
+
+        void addConnections(const std::vector<int> &outputIds, const std::vector<int> &inputIds);
+
+    private:
+
+        int lastLayerId;
+        std::map<int, Ptr<Layer>> layers;
+        std::map<int, std::vector<String>> layerOutputLabels;
+    };
+
+
+    class Net
+    {
+    public:
+
+        static Ptr<Net> create(Ptr<NetConfiguration> config);
+
+        virtual int getBlobId(int layerId, int outputId);
+
+        virtual int getBlobId(const String &blobName);
+
+        virtual void forward(std::vector<int, Ptr<Blob>> &inputBlobs, std::vector<int, Ptr<Blob>> &outputBlobs);
+
+        virtual void forward(int layer, std::vector<Ptr<Blob>> &layerOutputs);
+    };
+
+    class Importer
+    {
+    public:
+
+        virtual void populateNetConfiguration(Ptr<NetConfiguration> config) = 0;
+
+        virtual ~Importer();
+    };
+
+}
+}
+
+#endif
\ No newline at end of file
diff --git a/modules/dnn/src/dnn.cpp b/modules/dnn/src/dnn.cpp
new file mode 100644
index 000000000..81dd812ed
--- /dev/null
+++ b/modules/dnn/src/dnn.cpp
@@ -0,0 +1,28 @@
+#include "opencv2/dnn.hpp"
+
+namespace cv
+{
+namespace dnn
+{    
+
+    Blob::Blob(Mat &in) : _InputOutputArray(in)
+    {
+        
+    }
+
+    Blob::Blob(const Mat &in) : _InputOutputArray(in)
+    {
+
+    }
+
+    Blob::Blob(UMat &in) : _InputOutputArray(in)
+    {
+        
+    }
+
+    Blob::Blob(const UMat &in) : _InputOutputArray(in)
+    {
+
+    }
+}
+}
\ No newline at end of file