A process fault detection and diagnosis system (PFD&D) is proposed for complex chemical plants. The system combines an artificial neural network (ANN) based supplement of a fuzzy system in a block-oriented configuration. A methodology for designing the system is described. As a motivating example, a chemical plant with a recycle stream is considered. Faults in the supply of raw materials and in controllers are simulated. The performance of the system in handling simultaneous faults is also analysed. A comparison of the proposed approach is made with a classification method (ANNs) and inference methods (knowledge-based system). Results of system implementation in a fluidised bed coal gasifier at pilot plant scale are also shown.