A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the Space Shuttle Main Engine, the functional relationships among measured parameters is unknown and complex. Further-more, the number of possible input parameters is quite large. Many approaches have been proposed for input selection, but they are either not possible due to insufficient instrumentation, are subjective, or they do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms, they were employed in this study to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.