Recent work in metric learning has significantly improved the state-of-the-art in k-nearest neighbor classification. However, Support vector machines (with RBF kernels) are arguably the most popular class of classification algorithms that uses distance metrics to compare examples. In this talk I will introduce support vector metric learning (SVML), an algorithm that seamlessly combines both by learning a Mahalanobis metric at the same time as the RBF-SVM decision boundary. SVML is an effective tool for automatically pre-processing data sets for classification, as well as visualizing the structure of SVM decision boundaries. We demonstrate the capabilities (and shortcomings) of our algorithm on 10 benchmark data sets of varying sizes and difficulties.