We propose a scheme for pattern classifications in applications, which include ambiguous data, that is, where pattern occupy overlapping areas in the feature space. Such situations frequently occur with noisy data and/or where some features are unknown. We demonstrate that it is advantageous to first detect those ambiguous areas with the help of training data and then to re-classify those data in these areas as ambiguous before making class predictions on test sets. This scheme is demonstrated with a simple example and benchmarked on two real world applications.