This paper deals with a fault detection and isolation scheme for a DC motor driven centrifugal pump system. The emphasis is placed on the design and implementation of the residual generatorm, based on parity relation, that provides decision logic unit with residuals that will be further processed to detect and isolate three important faults in the system;brush fault, impeller fault, and the speed sensor fault. Two process faults are modelled as multiplicative type faults, while the sensor fault as an additive one. With multiplicative fault, the implementation of the residual generator needs the time varying transformation matrix that must be computed on-line. Typical implementation methods lack in generality because only a numerical approximation around the assumed fault levels is employed. In this paper, a new implementation method using well tranined neural network is proposed to improve the generality of the residual generator. Application results show that the fault detection and isolation scheme with the proposed residual generator effectively isolates three major faults in the centrifugal pump system even with a wide range of fault magnitude.