diff --git a/modules/dnn/src/op_inf_engine.cpp b/modules/dnn/src/op_inf_engine.cpp
index 60da9d14b3..b03a67d5f3 100644
--- a/modules/dnn/src/op_inf_engine.cpp
+++ b/modules/dnn/src/op_inf_engine.cpp
@@ -361,10 +361,20 @@ void InfEngineBackendNet::initPlugin(InferenceEngine::ICNNNetwork& net)
 {
     CV_Assert(!isInitialized());
 
-    InferenceEngine::StatusCode status;
-    InferenceEngine::ResponseDesc resp;
+    static std::map<std::string, InferenceEngine::InferenceEnginePluginPtr> sharedPlugins;
+    std::string deviceName = InferenceEngine::getDeviceName(targetDevice);
+    auto pluginIt = sharedPlugins.find(deviceName);
+    if (pluginIt != sharedPlugins.end())
+    {
+        enginePtr = pluginIt->second;
+    }
+    else
+    {
+        enginePtr = InferenceEngine::PluginDispatcher({""}).getSuitablePlugin(targetDevice);
+        sharedPlugins[deviceName] = enginePtr;
+    }
+    plugin = InferenceEngine::InferencePlugin(enginePtr);
 
-    plugin = InferenceEngine::PluginDispatcher({""}).getSuitablePlugin(targetDevice);
     if (targetDevice == InferenceEngine::TargetDevice::eCPU)
     {
 #ifdef _WIN32
@@ -374,18 +384,17 @@ void InfEngineBackendNet::initPlugin(InferenceEngine::ICNNNetwork& net)
         InferenceEngine::IExtensionPtr extension =
             InferenceEngine::make_so_pointer<InferenceEngine::IExtension>("libcpu_extension.so");
 #endif  // _WIN32
-        status = plugin->AddExtension(extension, &resp);
-        if (status != InferenceEngine::StatusCode::OK)
-            CV_Error(Error::StsAssert, resp.msg);
+        plugin.AddExtension(extension);
     }
-    status = plugin->LoadNetwork(net, &resp);
-    if (status != InferenceEngine::StatusCode::OK)
-        CV_Error(Error::StsAssert, resp.msg);
+    netExec = plugin.LoadNetwork(net, {});
+    infRequest = netExec.CreateInferRequest();
+    infRequest.SetInput(inpBlobs);
+    infRequest.SetOutput(outBlobs);
 }
 
 bool InfEngineBackendNet::isInitialized()
 {
-    return (bool)plugin;
+    return (bool)enginePtr;
 }
 
 void InfEngineBackendNet::addBlobs(const std::vector<Ptr<BackendWrapper> >& ptrs)
@@ -399,10 +408,7 @@ void InfEngineBackendNet::addBlobs(const std::vector<Ptr<BackendWrapper> >& ptrs
 
 void InfEngineBackendNet::forward()
 {
-    InferenceEngine::ResponseDesc resp;
-    InferenceEngine::StatusCode status = plugin->Infer(inpBlobs, outBlobs, &resp);
-    if (status != InferenceEngine::StatusCode::OK)
-        CV_Error(Error::StsAssert, resp.msg);
+    infRequest.Infer();
 }
 
 Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob)
diff --git a/modules/dnn/src/op_inf_engine.hpp b/modules/dnn/src/op_inf_engine.hpp
index a61678cab2..075c1be849 100644
--- a/modules/dnn/src/op_inf_engine.hpp
+++ b/modules/dnn/src/op_inf_engine.hpp
@@ -89,7 +89,10 @@ private:
     InferenceEngine::BlobMap allBlobs;
     InferenceEngine::TargetDevice targetDevice;
     InferenceEngine::Precision precision;
-    InferenceEngine::InferenceEnginePluginPtr plugin;
+    InferenceEngine::InferenceEnginePluginPtr enginePtr;
+    InferenceEngine::InferencePlugin plugin;
+    InferenceEngine::ExecutableNetwork netExec;
+    InferenceEngine::InferRequest infRequest;
 
     void initPlugin(InferenceEngine::ICNNNetwork& net);
 };
diff --git a/modules/dnn/test/test_layers.cpp b/modules/dnn/test/test_layers.cpp
index 5cbfba5517..593864822c 100644
--- a/modules/dnn/test/test_layers.cpp
+++ b/modules/dnn/test/test_layers.cpp
@@ -887,6 +887,31 @@ TEST(Test_DLDT, fused_output)
     ASSERT_NO_THROW(net.forward());
     LayerFactory::unregisterLayer("Unsupported");
 }
+
+TEST(Test_DLDT, multiple_networks)
+{
+    Net nets[2];
+    for (int i = 0; i < 2; ++i)
+    {
+        nets[i].setInputsNames(std::vector<String>(1, format("input_%d", i)));
+
+        LayerParams lp;
+        lp.set("kernel_size", 1);
+        lp.set("num_output", 1);
+        lp.set("bias_term", false);
+        lp.type = "Convolution";
+        lp.name = format("testConv_%d", i);
+        lp.blobs.push_back(Mat({1, 1, 1, 1}, CV_32F, Scalar(1 + i)));
+        nets[i].addLayerToPrev(lp.name, lp.type, lp);
+        nets[i].setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
+        nets[i].setInput(Mat({1, 1, 1, 1}, CV_32FC1, Scalar(1)));
+    }
+    Mat out_1 = nets[0].forward();
+    Mat out_2 = nets[1].forward();
+    // After the second model is initialized we try to receive an output from the first network again.
+    out_1 = nets[0].forward();
+    normAssert(2 * out_1, out_2);
+}
 #endif  // HAVE_INF_ENGINE
 
 // Test a custom layer.