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Over the last decade, support vector machine classifiers (SVMs) have demonstrated superior generalization performance to many other classification techniques in a variety of application areas. However, SVMs have an inability to provide an explanation, or comprehensible justification, for the solutions they reach. It has been shown that the ‘black-box’ nature of techniques like artificial neural networks (ANNs) is one of the main obstacles impeding their practical application. Therefore, techniques for rule extraction from ANNs, and recently from SVMs, were introduced to ameliorate this problem and aid in the explanation of their classification decisions. In this paper, we conduct a formal review of the area of rule extraction from SVMs. The review provides a historical perspective for this area of research and conceptually groups and analyzes the various techniques. In particular, we propose two alternative groupings …