Classical prediction error approaches for the identification of non-linear polynomial NARX/NARMAX models often yield unsatisfactory results for long-range prediction or simulation purposes, mainly due to incorrect or redundant model structure selection. The paper discusses some limitations of the standard approach and suggests two modi. cations: namely, a new index, based on the simulation error, is employed as the regressor selection criterion and a pruning mechanism is introduced in the model selection algorithm. The resulting algorithm is shown to be effective in the identification of compact and robust models, generally yielding model structures closer to the correct ones. Computational issues are also discussed. Finally, the identification algorithm is tested on a long-range prediction benchmark application.