This thesis investigates the applicability of backpropagation neural networks to production order dispatching in a dynamic, single machine shop where the achievement of multiple performance measures is desired. There has been relatively little research done in this area so the objectives center around the determination of information and parameters which lead to improved network performance with respect to learning as well as decision making. Results of the research showed that many of the qualities inherent to backpropagation neural networks were compatible with the requirements of the dispatching activity. The networks that were trained and tested had the ability to implicitly map the complex functional relationships between inputs reflecting system status and desired performance and outputs which represented appropriate coefficients used to determine job priority. Once trained they displayed good generalization capabilities when exposed to information they had never been exposed to before. Most importantly, they provided the basis for a complex dispatching procedure which utilized considerable shop floor information to make completely dynamic, real time dispatching decisions. Guidelines and generalizations for similar applications were developed including: input selection and presentation formats, effective training parameters, the effect of using purely dynamic vs. historical data as shop status inputs, the effect of compromising desired performance measure inputs, and the effect of changes in the underlying shop parameters. / M.S.