Abstract: Multivariate statistical process control (MSPC) has been successfully applied to performance monitoring and fault diagnosis for chemical processes. However, traditional MSPC are based upon the assumption that the separated latent variables must be subject to normal probability distribution, which sometimes can not be satisfied. In this paper, a novel method combining principal component analysis (PCA) and independent component analysis (ICA) is proposed to model non-Gaussian data from industry and improve the monitoring performance of process. In order to deal with the uncertainty of probability distribution within the independent component, a kind of classifier referred to as support vector classifier is used for classifying the abnormal modes. Simulation result for a nonisothermal continuous stirred-tank reactor (CSTR) by the presented method verifies the effectiveness of ICA-based algorithm.