Continuous Stirred Tank Reactor (CSTR) here is considered as a nonlinear process. The CSTR is widely used in many chemical plants. Due to changes in process parameters the accuracy of final product can be reduced. In order to get accurate final product the faults developed in CSTR during the chemical reaction need to be diagnosed. If not, the faults may lead to degrade the performance of the system. For this purpose there are various fault diagnosis methods are to be considered. Among the methods, the neural network predictive controller can be used to detect faults in CSTR. Servo response is performed to understand the behavior of CSTR. By detecting various faults and with suitable control techniques, the accuracy of the desirable products in CSTR can be improved