Sufficient dimension reduction (SDR) ideas are used for supervised dimension reduction in regression problems. Support Vector Machine (SVM) algorithms belong to the class of machine learning techniques which are used for classification. In this talk we discuss Principal Support Vector Machine (PSVM) a method which utilizes SVM to achieve sufficient dimension reduction. PSVM has several advantages over existing methodology for sufficient dimension reduction, with the most important one being the fact that we can do linear and nonlinear dimension reduction under a unified framework. We will give an overview of basic theoretical and simulation results. We also discuss extensions where different machine learning algorithms can be used for improving the performance of PSVM.