This paper discusses visual methods that can be used to understand and interpret the results of classification using support vector machines (SVM). SVM induction algorithms build pattern classifiers by identifying a maximal margin separating hyperplane from training examples in high dimensional pattern spaces or spaces induced by suitable nonlinear kernel transformations over pattern spaces. SVM have been demonstrated to be quite effective in a number of practical pattern classification tasks. Since the separating hyperplane is specified in terms of a large number of variables, there is a need for techniques that help users understand the SVM classifiers generated from particular high-dimensional data sets. We demonstrate the use of projection-based tour methods to gain useful insights into SVM classifiers.