The Support Vector method (SVM) is a new general method of function estimation which does not depend explicitly on the dimensionality of the problem. It has been applied to pattern recognition, regression estimation, and density estimation problems as well as to solving linear operator equations. In this talk the idea of the SVM, as well as some elements of its theory will be presented. In particular it will be shown that the generalization ability of the SVM is based on factors that classical statistics does not take into account. Therefore using the SVM one can generalize well in a high dimensional space on the basis of a small number of examples. In the presentation some examples of solving various pattern recognition and regression estimation problems will be shown and the results obtained will be comapared with the results obtained using existing state-of-the-art techniques.