Support vector machines and rough set theory are two classification techniques. Support vector machines can use continuous input variables and transform them to higher dimensions, so that classes can be linear separable. A support vector machine attempts to find the hyperplane that maximizes the margin between classes. This paper shows how the classification obtained from a support vector machine can be represented using interval or rough sets. Such a formulation is especially useful for soft margin classifiers.