Multivariate statistical process monitoring (MSPM) methods are powerful tools for detecting faults in industrial systems. However, industrial processes are often subjected to dynamic changes. This dynamic behavior is mainly due to set-point changes and nonlinearities. Because of the nonlinearity of processes, the performance of the classical MSPM methods, which are mainly based on the linearity assumption, becomes unsatisfactory, since the process characteristics will change from one operating point to another. The main objective of the work is to develop an efficient fault detection technique for complex industrial systems, using process historical data and considering the nonlinear behavior of the process. In the proposed approach, the nonlinear system is assumed to be linear around the operating points and therefore considered as a piecewise linear system corresponding to each operating mode. The performance and effectiveness of this approach are demonstrated using data obtained from a paper machine and compared with an available method.