This article presents a regression-based monitoring approach for diagnosing abnormal conditions in complex chemical process systems. Such systems typically yield process variables that may be both Gaussian and non-Gaussian distributed. The proposed approach utilizes the statistical local approach to monitor parametric changes of the latent variable model that is identified by a revised non-Gaussian regression algorithm. Based on a numerical example and recorded data from a fluidized bed reactor, the article shows that the proposed approach is more sensitive when compared to existing work in this area. A detailed analysis of both application studies highlights that the introduced non-Gaussian monitoring scheme extracts latent components that provide a better approximation of non-Gaussian source signal and/or is more sensitive in detecting process abnormities. (c) 2013 American Institute of Chemical Engineers AIChE J, 60: 148-159, 2014