diff --git a/test/test-svm1.js b/test/test-svm1.js
new file mode 100644
index 0000000..2c69f1e
--- /dev/null
+++ b/test/test-svm1.js
@@ -0,0 +1,46 @@
+var x = [[0.4, 0.5, 0.5, 0.,  0.,  0.],
+         [0.5, 0.3,  0.5, 0.,  0.,  0.01],
+         [0.4, 0.8, 0.5, 0.,  0.1,  0.2],
+         [1.4, 0.5, 0.5, 0.,  0.,  0.],
+         [1.5, 0.3,  0.5, 0.,  0.,  0.],
+         [0., 0.9, 1.5, 0.,  0.,  0.],
+         [0., 0.7, 1.5, 0.,  0.,  0.],
+         [0.5, 0.1,  0.9, 0.,  -1.8,  0.],
+         [0.8, 0.8, 0.5, 0.,  0.,  0.],
+         [0.,  0.9,  0.5, 0.3, 0.5, 0.2],
+         [0.,  0.,  0.5, 0.4, 0.5, 0.],
+         [0.,  0.,  0.5, 0.5, 0.5, 0.],
+         [0.3, 0.6, 0.7, 1.7,  1.3, -0.7],
+         [0.,  0.,  0.5, 0.3, 0.5, 0.2],
+         [0.,  0.,  0.5, 0.4, 0.5, 0.1],
+         [0.,  0.,  0.5, 0.5, 0.5, 0.01],
+         [0.2, 0.01, 0.5, 0.,  0.,  0.9],
+         [0.,  0.,  0.5, 0.3, 0.5, -2.3],
+         [0.,  0.,  0.5, 0.4, 0.5, 4],
+         [0.,  0.,  0.5, 0.5, 0.5, -2]];
+// Only binary classification here: Feature Y=-1->false, 1->true
+var y =  [-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1,1,1,1,1,1];
+
+var model = ml.learn({
+    algorithm : ml.ML.SVM,
+    x : x,
+    y : y,
+    
+    C : 1, // default : 1.0. C in SVM.
+    tol : 1e-4, // default : 1e-4. Higher tolerance --> Higher precision
+    max_passes : 200, // default : 20. Higher max_passes --> Higher precision
+    alpha_tol : 1e-5, // default : 1e-5. Higher alpha_tolerance --> Higher precision
+
+    //kernel : { type: "polynomial", c: 1, d: 5}
+    kernel : { type: "rbf", sigma:0.5 }
+});
+
+print(toJSON(model).length+' Bytes')
+
+// print(model)
+a = [
+  [1.3,  1.7,  0.5, 0.5, 1.5, 0.4],
+  [0.05,  0.1,  0.5, 0.7, 0.4, -1.4]
+]
+print(ml.classify(model,x));
+print(ml.classify(model,a));