In this paper, a new monitoring system is proposed by connecting different research areas, such as statistical monitoring, as well as knowledge-based and history-based systems. Tools such as adaptive principal components analysis (APCA), fuzzy-logic (FL) methods, and artificial neural network (ANN) methods are integrated to develop an efficient fault detection, isolation, and estimation (FDIE) system, especially for large chemical plants. It is capable of detecting, classifying, and estimating several faulty process elements. The information given by this new monitoring system is able to support the proper decisions for connecting and transforming an existing decentralized control strategy to a fault-tolerant method, based on an on-line reconfiguration. Thus, the obtained FDIE system is a valuable tool that is able to improve the overall performance of large and complex nonlinear controlled plants. In this case, inherent faults in sensors and actuators are analyzed. The FDIE system is tested for single as well as sequential abnormal events on a pulp mill benchmark, which is one of the biggest processes in the fault-tolerant control (FTC) that is integrated into the FDIE areas analyzed in the literature. A complete set of simulation results, evaluated by different indexes, together with cost analysis about the process operational profits with and without an FDIE system, are used here, to demonstrate the effectiveness of the proposed methodology.