diff --git a/modules/core/perf/perf_math.cpp b/modules/core/perf/perf_math.cpp
index de97210797..267cc9c409 100644
--- a/modules/core/perf/perf_math.cpp
+++ b/modules/core/perf/perf_math.cpp
@@ -8,6 +8,9 @@ using std::tr1::get;
 
 typedef perf::TestBaseWithParam<size_t> VectorLength;
 
+typedef std::tr1::tuple<int, int> MaxDim_MaxPoints_t;
+typedef perf::TestBaseWithParam<MaxDim_MaxPoints_t> MaxDim_MaxPoints;
+
 PERF_TEST_P(VectorLength, phase32f, testing::Values(128, 1000, 128*1024, 512*1024, 1024*1024))
 {
     size_t length = GetParam();
@@ -21,3 +24,48 @@ PERF_TEST_P(VectorLength, phase32f, testing::Values(128, 1000, 128*1024, 512*102
 
     SANITY_CHECK(angle, 5e-5);
 }
+
+PERF_TEST_P( MaxDim_MaxPoints, kmeans,
+             testing::Combine( testing::Values( 16, 32, 64 ),
+                               testing::Values( 300, 400, 500) ) )
+{
+    RNG& rng = theRNG();
+    const int MAX_DIM = get<0>(GetParam());
+    const int MAX_POINTS = get<1>(GetParam());
+    const int attempts = 5;
+
+    Mat labels, centers;
+    int i,  N = 0, N0 = 0, K = 0, dims = 0;
+    dims = rng.uniform(1, MAX_DIM+1);
+    N = rng.uniform(1, MAX_POINTS+1);
+    N0 = rng.uniform(1, MAX(N/10, 2));
+    K = rng.uniform(1, N+1);
+
+    Mat data0(N0, dims, CV_32F);
+    rng.fill(data0, RNG::UNIFORM, -1, 1);
+
+    Mat data(N, dims, CV_32F);
+    for( i = 0; i < N; i++ )
+        data0.row(rng.uniform(0, N0)).copyTo(data.row(i));
+
+    declare.in(data);
+
+    TEST_CYCLE()
+    {
+        kmeans(data, K, labels, TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 30, 0),
+               attempts, KMEANS_PP_CENTERS, centers);
+    }
+
+    Mat clusterPointsNumber = Mat::zeros(1, K, CV_32S);
+
+    for( i = 0; i < labels.rows; i++ )
+    {
+        int clusterIdx = labels.at<int>(i);
+        clusterPointsNumber.at<int>(clusterIdx)++;
+    }
+
+    Mat sortedClusterPointsNumber;
+    cv::sort(clusterPointsNumber, sortedClusterPointsNumber, cv::SORT_EVERY_ROW + cv::SORT_ASCENDING);
+
+    SANITY_CHECK(sortedClusterPointsNumber);
+}