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This thesis evaluates the capabilities of artificial neural networks in forecasting the take-rates of the Voluntary Separations Incentive/Special Separations Benefit (VSI/SSB) programs for male, Marine Corps Enlisted Personnel in the grades of E-5 and E-6. The Artificial Neural Networks models are compared with the forecasting abilities of a classical regression model. The data are taken from the Headquarters Marine Corps Enlisted Master File which contains military and personal background on each enlisted member of the United States Marine Corps. The classical regression model is a casual model constructed based upon the theory of occupational job choice. The neural network models are presented with all available data elements. Empirical results indicate that artificial neural networks provide forecasting results at least as good as, if not better than, those obtained using classical regression techniques. However, artificial neural networks are limited in their usefulness for policy analysis. Neural networks, Modeling techniques, Voluntary separation programs, VSI, SSB, Marine Corps separations incentives.