This exploratory study assesses the accuracy of backpropagation neural networks in predicting sortie generations, given pre-specified levels of air base resources. Single hidden layer networks and two-way interaction regression metamodels were fitted to simulated data previously generated by way of a factional design for ten factors at two levels, and subsequently tested (cross-validated) via an independent testing sample. It was determined that regression metamodels were generally superior in predicting unseen cases, while their network counterparts exhibited far better goodness-of-fit characteristics. The research consistently emphasizes that goodness-of-fit in no way necessarily implies goodness-of-prediction, in that different non-equivalent statistical measures are required to assess both these phenomena. In spite of their relatively poor performance in predicting the test sample used in this study, experimental results indicate that future research focused on applying neural network modeling techniques to sortie generation prediction and the identification of critical air base resources is warranted.