Abstract: Orthogonal design can find out the optimal combination of factors with fewer experiments.Besides dealing with small samples，Support Vector Machine（SVM） has good generalization ability and is immune to the restriction of the data dimension.In this paper，a method using SVM and orthogonal design is proposed，in which training and testing are arranged according to the feature numbers of datasets and the structure of orthogonal table，and finally，experiments are carried out on the selected subsets of features.The results indicate that the proposed method can not only discard the redundant features but also gain better classification accuracy than that on the datasets with full features.