The selection of kernel functions has an important influence on the classification results of support vector machines. This paper proposed a kernel functions selection method based on rank sum test in order to enhance the selection objectivity, where the error degree adopted in the rank sum test was represented by the distance between the error instance and the interface of support vectors. By comparing with other statistical methods, such as K-folding cross validation and paired t test, the classification abilities of nine common kernel functions were quantitatively studied based on 15 datasets. Different from parameter test methods, the rank sum test does not assume the data distribution(in some cases data cannot satisfy the assumed distribution), the experimental data proves that the rank sum test not only can objectively evaluate the classification abilities of kernel functions, but also can produce better selection results on some data sets.