The purpose of this paper is to present a learning algorithm to classify data with nonlinear characteristics. The Support Vector Machine (SVM) is a novel type of learning machine based on statistical learning theory <Vapnik, 1998>. The support vector machine (SVM) implements the following idea: It maps the input vector X into a high-dimensional feature space Z through some nonlinear mapping, chosen a priori. In this space, an optimal separating hyperplane is constructed to separate data groupings. The support vector machine (SVM) learning method can be used to classify seismic data patterns for exploration and reservoir characterization applications. The SVM is particularly good at classifying data with nonlinear characteristics. As an example the SVM method is applied to AVO classification of gas sand and wet sand.