A novel supervised kernel local linear embedding(SKLLE) method is introduced to facial expression recognition,which maps face images to a high dimensional kernel space through nonlinear kernel mapping,then fuses prior class-label information and nonlinear facial expression submanifold of real face images to extract discriminative features for expression classification.SKLLE can not only gain a perfect approximation of facial expression manifold,and enhance local within-class relations,but also can do well on the new samples.The experimental results on JAFFE database show that the proposed method can achieve the highest recognition rate(100%) using only 2D embedding feature vectors,which improves face expression classification performance effectively.