We propose several means for improving the performance and training of neural networks for classification. We use crossvalidation as a tool for optimizing network parameters and architecture. We show further that the remaining residual “generalization” error can be reduced by invoking ensembles of similar networks. Zndex Terms-Crossvalidation, fault tolerant computing, neural networks, N-version programming.