Aim at the defect that weighted

k

-nearest neighbor method classifies one test sample only using the class information of its

k

-nearest samples

a sequential weighted k-nearest neighbor classification method is proposed in this paper.Not only the class information offered by

k

-nearest neighbor points of test sample but also the class information of previous test sample is used for classification in the proposed method.So its decision-making processing is more reasonable and effective.The experimental results of facial expression recognition in Cohn-Kanade face database show the method is better than weighted k-nearest neighbor method for the classification of sequential samples.