diff --git a/tests/unit/multiclass/LaRank_unittest.cc b/tests/unit/multiclass/LaRank_unittest.cc
new file mode 100644
index 00000000000..11a44c8d987
--- /dev/null
+++ b/tests/unit/multiclass/LaRank_unittest.cc
@@ -0,0 +1,87 @@
+#include <shogun/labels/MulticlassLabels.h>
+#include <shogun/features/DenseFeatures.h>
+#include <shogun/kernel/GaussianKernel.h>
+#include <shogun/multiclass/LaRank.h>
+#include <shogun/base/init.h>
+#include <gtest/gtest.h>
+
+using namespace shogun;
+
+TEST(LaRank,train)
+{
+	index_t num_vec=10;
+	index_t num_feat=3;
+	index_t num_class=num_feat; // to make data easy
+	float64_t distance=15;
+
+	// create some linearly seperable data
+	SGMatrix<float64_t> matrix(num_class, num_vec);
+	SGMatrix<float64_t> matrix_test(num_class, num_vec);
+	CMulticlassLabels* labels=new CMulticlassLabels(num_vec);
+	CMulticlassLabels* labels_test=new CMulticlassLabels(num_vec);
+	for (index_t i=0; i<num_vec; ++i)
+	{
+		index_t label=i%num_class;
+		for (index_t j=0; j<num_feat; ++j)
+		{
+			matrix(j,i)=CMath::randn_double();
+			matrix_test(j,i)=CMath::randn_double();
+			labels->set_label(i, label);
+			labels_test->set_label(i, label);
+		}
+
+		/* make sure data is linearly seperable per class */
+		matrix(label,i)+=distance;
+		matrix_test(label,i)+=distance;
+	}
+	//matrix.display_matrix("matrix");
+	//labels->get_int_labels().display_vector("labels");
+
+	// shogun will now own the matrix created
+	CDenseFeatures<float64_t>* features=new CDenseFeatures<float64_t>(matrix);
+	CDenseFeatures<float64_t>* features_test=
+			new CDenseFeatures<float64_t>(matrix_test);
+
+	// create three labels
+	for (index_t i=0; i<num_vec; ++i)
+		labels->set_label(i, i%num_class);
+
+	// create gaussian kernel with cache 10MB, width 0.5
+	CGaussianKernel* kernel = new CGaussianKernel(10, 0.5);
+	kernel->init(features, features);
+
+	// create libsvm with C=10 and train
+	CLaRank* svm = new CLaRank(10, kernel, labels);
+	svm->train();
+	svm->train();
+
+	// classify on training examples
+	CMulticlassLabels* output=(CMulticlassLabels*)svm->apply();
+	output->get_labels().display_vector("batch output");
+
+	/* assert that batch apply and apply(index_t) give same result */
+	SGVector<float64_t> single_outputs(output->get_num_labels());
+	for (index_t i=0; i<output->get_num_labels(); ++i)
+		single_outputs[i]=svm->apply_one(i);
+
+	//single_outputs.display_vector("single_outputs");
+
+	for (index_t i=0; i<output->get_num_labels(); ++i)
+		EXPECT_EQ(output->get_label(i), single_outputs[i]);
+
+	// predict test labels (since data is easy this has to be correct
+	CMulticlassLabels* output_test=
+			(CMulticlassLabels*)svm->apply(features_test);
+	//labels_test->get_labels().display_vector("labels_test");
+	//output_test->get_labels().display_vector("output_test");
+
+	for (index_t i=0; i<output->get_num_labels(); ++i)
+		EXPECT_EQ(labels_test->get_label(i), output_test->get_label(i));
+
+	// free up memory
+	SG_UNREF(output);
+	SG_UNREF(labels_test);
+	SG_UNREF(output_test);
+	SG_UNREF(svm);
+}
+