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Due to the variability in performance among cochlear implant (CI) patients, it is becoming increasingly important to find ways to optimally fit patients with speech processing strategies. This paper proposes an approach based on neural networks, which can be used to automatically optimize the performance of CI patients. The neural network model is implemented in two stages. In the first stage, a neural network is trained to mimic the CI patient's performance on the vowel identification task. The trained neural network is then used in the second stage to adjust a free parameter to improve vowel recognition performance for each individual patient. The parameter examined in this study was a weighting function applied to the compressed channel amplitudes extracted from a 6-channel continuous interleaved sampling (CIS) strategy. Two types of weighting functions were examined, one which assumed channel interaction, and one which assumed no interaction between channels. Results showed that the neural network models closely matched the performance of five Med-El/CIS-Link implant patients. The resulting weighting functions obtained after neural network training improved vowel performance, with the larger improvement (4%) attained by the weighting function which modeled channel interaction.