This paper describes the recognition of container code characters in the project Mocont-II, where container images are taken in largely varying light situations. The recognition system has to deal with gray-level characters showing a wide variability of brightness and contrast, varying inclination, segmentation uncertainties, damaged characters and the presence of shadows. Different sets of features were extracted directly from gray-level images, and a minimum distance classifier with a weighted metric was used for recognition. To achieve good recognition performances, the feature weights and the prototype sets were optimized by a new gradient-based learning algorithm that maximizes a fuzzy recognition rate functional.