The synthesis of a real-time diagnostic expert system to monitor plant performance and identify faults in the event of process failure as well as signal potential failures is described. The crucial features which have been included are system reliability, use of dynamic trends in data and parameters to identify problem as well as the ability to handle complex knowledge. The emphasis here is on how a dynamic simulator is used to incorporate a learning algorithm based on a fuzzy set covering method for formulating a knowledge model of the operational characteristics, which enables debugging and testing of the system to be carried out continuously. The procedure is illustrated by reference to the operation of refinery crude oil distillation columns.