Neural nets, fuzzy sets and graph theories have been used individually to the detection and diagnosis of faults during process operation, as well as assessment of potential hazards and operability problems in design, but all three approaches have their limitations. This study builds on the individual strengths and seeks to blend the three approaches to compensate for inadequacies by adopting the algorithm from each of the method into a composite procedure for capturing process knowledge. A fuzzy qualitative approach is proposed for interpreting dynamic data including patterns of change in process variables, meeting process constraints and closed-loop dynamic behaviour so that a neural network for fault diagnosis can deal with dynamic data effectively. The neural network teaming algorithm is adapted to train a fuzzy relation matrix between two fuzzy sets which provides a covering model for fault diagnosis. Back Propagation Neural Network (BPNN), single layer perceptron (PCT) and Fuzzy Set Covering(FSC) methods are examined with respect to the fault diagnosis of a fluid catalytic cracking process. While the three methods have similar capabilities in identifying significant disturbances and faults, BPNN is better than PCT and FSC in isolating small disturbances. These concepts enable a new fuzzy-SDG approach to be developed which uses fuzzy membership functions to replace the sigmoid function in the nodes with the effect weights of the graph being obtained by training. With this technique it is possible to extract qualitative knowledge from plant records to model disturbance propagation in the process.