Ensemble encoding is a distributed data representation scheme that uses multiple, overlapping receptive fields to encode inputs to MLP networks. The number, placement, and form of the receptive fields can have a significant impact on the effectiveness of ensemble encoding. We present four approaches, two based on descriptive statistics, and two based on clustering, for optimizing receptive field configuration, and compare their performance on three benchmark data sets. To ensure fairness of comparison and reduce the effects of random noise, leave-one-out cross-validation is employed, and, a test of statistical significance is applied to the results.