This paper introduces the theory and training algorithm of the support vector machine which is applied in nonlinear classification and recognition by the way of bringing in the concept such as structural risk minimization principle and optimal hyperplane

then a set of nonlinear binary samples are successfully classified by using different kernel functions

followed by discussion to the results.After that current multi-class classification algorithms and application areas are reviewed.Finally future developments are prospected.