Abstract: Statistical process monitoring (SPM) is widely used to improve process operation performance and produce consistently high-quality products for multivariate processes. However, existed SPM methods are frustrated by the dramatic nonlinear feature of some processes. Neural network is the most common tool dealing with nonlinear problems. This, a new process monitoring and fault diagnosis method is presented based on nonlinear principal component regress (NLPCR) algorithm integrated with radial basis function (RBF) network, which will evidently combine the advantages of multivariate projecting technology and the excellent approximation of RBF network. The results of the experiment indicate that the method is capable of monitoring the whole process operation performance, detecting the occurrence of abnormal conditions and then giving correct diagnosis result quickly and accurately.