In this paper, we propose M-estimator based robust kernels for support vector machine. The main motivation for our proposed kernels is that the sum of squared difference in the widely used gaussian radial basis function kernels is not robust to outlier or noise. In addition, inspired by using a robust loss function in support vector machine regression to control training error [Learning with kernels - support vector machines, regularization, optimzation and be-yond] and the idea of robust template matching with M-estimator [Fast algorithm for robust template matching with m-estimators], we apply M-estimator techniques to gaussian radial basis functions and form a new class of robust kernels for support vector machines. We test our proposed kernels in several classification benchmark datasets and experimental results show that SVM with proposed kernels are better than SVM with gaussian radial basis function kernels.