This thesis presents a commercial outcome prediction system (CPS) capable of predicting the likely future monetary return that would be generated by an invention. The CPS is designed to be used by university technology transfer offices for invention assessment purposes, and is based on the data from their historical invention cases. It is aimed at improving technology transfer offices' invention assessment performance. Using qualitative critical factors suggested by literature. a prototype CPS based on decision tree induction was developed. The prediction performance achieved by the prototype CPS was unreliable. Three surveys with various technology transfer offices were then performed, and the findings were incorporated into a final version of the CPS, which was based on neural networks. Subject to information obtained in the surveys, a number of potentially predictive attributes were proposed to form part of the predictor variables for the CPS. The CPS starts with a number of data reduction operations (based on principal component analysis and decision tree techniques), which identify the critical predictor variables. The CPS then uses five neural-network training algorithms to generate candidate classifiers, upon which the final classification is based. The prediction results achieved by the CPS were good and reliable. Additionally, the data reduction operations successfully captured the most discriminative invention attributes. The research demonstrated the potential or using the CPS for invention assessment. However, it requires sufficient historical data from the technology transfer office using it to provide accurate assessments.