This thesis describes research to implement a Bayesian belief network based expert system to solve a real-world diagnostic problem troubleshooting integrated circuit (IC) testing machines. Several models of the IC tester diagnostic problem were developed in belief networks, and one of these models was implemented using Symbolic Probabilistic Inference (SPI). The difficulties and advantages encountered in the process are described in this thesis. It was observed that modelling with interdependencies in belief networks simplified the knowledge engineering task for the IC tester diagnosis problem, by avoiding procedural knowledge and sticking just to diagnostic component's interdependencies. Several general model frameworks evolved through knowledge engineering to capture diagnostic expertise that facilitated expanding and modifying the networks. However, model implementation was restricted to a small portion of the modelling - contact resistance failures - because evaluation of the probability distributions could not be made fast enough to expand the code to a complete model with real-time diagnosis. Further research is recommended to create new methods, or refine existing methods, to speed evaluation of the models created in this research. If this can be done, more complete diagnosis can be achieved. / Graduation date: 1994