An expert system developed to identify input items to INIS database with a high probability of containing errors is described. The system employs a Knowledge Base constructed by the interpretation of a large number of intellectual choices or expert decisions made by human indexers and incorporated in the INIS database. On the basis of the descriptor indexing, the system checks the correctness of the categorization. A notable feature of the system is its capability of self improvement by the continuous updating of the Knowledge Base. The expert system has also been found to be extremely useful in identifying documents with poor indexing. 3 refs, 9 figs.