This paper deals with the advanced and developed methodology know for cancer multi classification using Support Vector Machine (SVM) for microarray gene expression cancer diagnosis, this is used for directing multicategory classification problems in the cancer diagnosis area. SVMs are an appropriate new technique for binary classification tasks, which is related to and contain elements of non-parametric applied statistics, neural networks and machine learning. SVMs can generate accurate and robust classification results on a sound theoretical basis, even when input data are non-monotone and non-linearly separable. The performance of SVM is evaluated for the multicategory classification on benchmark microarray data sets for cancer diagnosis, namely, the SRBCT Data set. The results indicate that SVM produces comparable or better classification accuracies when the data given as input are preprocessed. SVM delivers high performance with reduced training time and implementation complexity is less when compared to artificial neural networks methods like conventional backpropagation ANN and Linder’s SANN.