A range of accuracy indices for determining the optimal outputs from the classification of multispectral remotely sensed data is evaluated. Airborne Thematic Mapper imagery of semi-natural woodland was used in conjunction with an in situ data set. Indices of classification accuracy were unable to distinguish substantial differences in classified images because they are based only on errors of omission, accounting for only a proportion of the errors in classification. The Classification Success Index (CSI) is introduced here to estimate the overall effectiveness of classification, considering all output classes and using both errors of omission and commission from the error matrix. The Individual Classification Success Index (ICSI) is introduced which accounts for the classification success of a specific class. Finally, the Group Classification Success Index (GCSI) measures classification success for the most important classes in the area of interest. These new indices were found to offer considerable improvement over existing approaches.