In this paper a new approach to a fuzzy support vector machine (FSVM) for solving multi-class problems is presented. The developed algorithm combines two separate methods based on fuzzy support vector machine, one for solving two-class problems and the second for multi-class problems. The first method deals with the problem of selecting the best support vector machine (SVM) kernel function and the second method enables classification of unclassified regions that appear when classical SVM methods for solving multi-class problems are used. Presented tool has been subjected to the dataset from Kent Ridge Biomedical Data Set Repository and showed its superiority comparing with conventional SVM and FSVM methods.