To improve the statistical monitoring performance of complex chemical process, a new statistical process monitoring and fault identification method having the character of nonlinear which based on kernel independent component analysis (KICA) and kernel fisher discriminant analysis (KFDA) is proposed. KICA is used to establish the normal operating conditions and identify the fault. If a fault occurs, the nuclear fisher discriminant vector and feature vector F of the process data are extracted from the Fisher subspace. Thus, the batch normal or not can be detected by comparing distance with the predefined threshold. Comparing the present discriminant vector and the optimal discriminant vector of fault in historical data set, the similar degree can be identified. According to the similar degree, the perform fault can be diagnosed. The results of simulating demonstrate that the proposed method can efficient in detecting and diagnosing the malfunctions，with more accurate result.