Abstract: Neckline performance evaluation is an important part for knitted sweater.In order to implement classification automation of testing system,kernel Fisher discriminant analysis(KFDA) is proposed for identifying the grade of new testing samples.Classified samples are divided into two parts: training samples and testing samples.Input space is mapped into feature space by kernel function,projection vector is acquired and discriminant functions is constructed in feature space,subsequently,identification effect is verified by testing samples. Finally,identification effects of KFDA,back promulgation neural network(BPNN) and radial basis function neural network(RBFNN) are compared.Experiment results show that KFDA method is more effective for identification of new samples.