Support vector machines (SVMs) are supervised learning models traditionally employed for classification and regression analysis. Despite being one of the most popular classification models because of its strong performance empirically, understanding the knowledge captured in a SVM remains difficult. SVMs are typically applied in a black-box manner where the details of parameters tuning, training and even the final constructed model is hidden from the users. This is natural since these details are often complex and difficult to understand without proper visualization tools. However, such an approach often bring about various problems including trial-and-error tuning and suspicious users who are forced to trust these models blindly. The contribution of this paper is a visual analysis approach for building SVMs in an open-box manner. Our goal is to improve an analyst’s understanding of the SVM modeling process through a suite of visualization techniques that allow users to have full interactive visual control over the entire SVM training process. Our visual exploration tools have been developed to enable intuitive parameter tuning, training data manipulation, and rule extraction as part of the SVM training process.