Combined with the breast fine needle aspiration cytology,the SVM,K-Nearest Neighbor(K-NN) and Probabilistic Neural Network(PNN) are used to diagnose the breast cancer.The best overall accuracy reaches 96.24% via SVM with Sigmoid kernel by using 5-fold cross validation,and is superior to those of other classifiers including K-NN(95.37%) and PNN(95.09%).Support vector machine is capable of being used as a potential application tool for SVM-aided clinical breast cancer diagnosis.