An adaptive robust M-estimator for nonparametric nonlinear system identification is proposed. This M-estimator is optimal over a broad class of distributions in the sense of maximum likelihood estimation. The error distributions are described by the generalized exponential distribution family. It combines nonparametric regression techniques to form a powerful procedure for nonlinear system identification. The adaptive procedure's excellent performance characteristics are illustrated in a Monte Carlo study by comparing the results with previous methods.