On-line fault diagnosis is complicated due to the difficulty in acquiring knowledge about multiple cause-effect relationships, treating closed-loop dynamic responses while at the same time trying to continuously improve by updating performance by incorporating an adaptive facility. This paper reports the use of a dynamic simulator incorporating a fuzzy neural network learning algorithm to build the fault models. The effectiveness of the approachis illustrated by reference to the refinery residue fluid catalytic cracking process.