diff --git a/tests/unit/base/Serialization_unittest.cc b/tests/unit/base/Serialization_unittest.cc
index ee428c95fdd..b4fd84e0e47 100644
--- a/tests/unit/base/Serialization_unittest.cc
+++ b/tests/unit/base/Serialization_unittest.cc
@@ -8,8 +8,12 @@
  */
 
 #include <shogun/base/init.h>
+#include <shogun/labels/BinaryLabels.h>
 #include <shogun/labels/MulticlassLabels.h>
 #include <shogun/io/SerializableAsciiFile.h>
+#include <shogun/classifier/svm/LibLinear.h>
+#include <shogun/features/DataGenerator.h>
+#include <shogun/features/DenseFeatures.h>
 #include <gtest/gtest.h>
 
 using namespace shogun;
@@ -68,4 +72,63 @@ TEST(Serialization,multiclass_labels)
 	SG_UNREF(labels);
 }
 
+TEST(Serialization, liblinear)
+{
+	index_t num_samples = 50;
+	CMath::init_random(13);
+	SGMatrix<float64_t> data =
+		CDataGenerator::generate_gaussians(num_samples, 2, 2);
+	CDenseFeatures<float64_t> features(data);
+
+	SGVector<index_t> train_idx(num_samples), test_idx(num_samples);
+	SGVector<float64_t> labels(num_samples);
+	for (index_t i = 0, j = 0; i < data.num_cols; ++i)
+	{
+		if (i % 2 == 0)
+			train_idx[j] = i;
+		else
+			test_idx[j++] = i;
+
+		labels[i/2] = (i < data.num_cols/2) ? 1.0 : -1.0;
+	}
+
+	CDenseFeatures<float64_t>* train_feats = (CDenseFeatures<float64_t>*)features.copy_subset(train_idx);
+	CDenseFeatures<float64_t>* test_feats =  (CDenseFeatures<float64_t>*)features.copy_subset(test_idx);
+
+	CBinaryLabels* ground_truth = new CBinaryLabels(labels);
+
+	CLibLinear* liblin = new CLibLinear(1.0, train_feats, ground_truth);
+	liblin->set_epsilon(1e-5);
+	liblin->train();
+
+	CBinaryLabels* pred = CBinaryLabels::obtain_from_generic(liblin->apply(test_feats));
+	for (int i = 0; i < num_samples; ++i)
+		EXPECT_EQ(ground_truth->get_int_label(i), pred->get_int_label(i));
+
+	/* save liblin */
+	const char* filename="trained_liblin.txt";
+	CSerializableAsciiFile* file=new CSerializableAsciiFile(filename, 'w');
+	liblin->save_serializable(file);
+	file->close();
+	SG_UNREF(file);
+
+	/* load liblin */
+	file=new CSerializableAsciiFile(filename, 'r');
+	CLibLinear* liblin_loaded=new CLibLinear();
+	liblin_loaded->load_serializable(file);
+	file->close();
+	SG_UNREF(file);
+
+	/* classify with the deserialized model */
+	pred = CBinaryLabels::obtain_from_generic(liblin_loaded->apply(test_feats));
+	for (int i = 0; i < num_samples; ++i)
+		EXPECT_EQ(ground_truth->get_int_label(i), pred->get_int_label(i));
+
+	SG_UNREF(liblin_loaded);
+	SG_UNREF(liblin);
+	SG_UNREF(train_feats);
+	SG_UNREF(test_feats);
+	SG_UNREF(pred);
+}
+