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This paper presents the details of the study to optimize and arrive at a design base for a vacuum pump in an automotive engine using resilient back propagation algorithm for Artificial Neural Networking (ANN). The reason for using neural networks is to capture the accuracy of experimental data while saving computational time, so that system simulations can be performed within a reasonable time frame. Vacuum Pump is an engine driven part. Design and optimization of a vacuum pump in an automotive engine is crucial for development. The NN predicted values had a good correlation with the actual values of tested proto sample. The design optimization by means of this study has served the purpose of generating the data base for future development of different capacity vacuum pumps.