With the widespread computerization in business, government, and science, the efficient and effective discovery of interesting information from large databases becomes essential. Previous studies on data mining have been focused on the discovery of knowledge at single conceptual level, either at the primitive level or at a rather high conceptual level. This paper presents an algorithm based on the AFOPT algorithm for multi-level databases that uses the benefits of multileveled databases, by using the information gained by studying items from one concept level for the study of the items from the following concept levels.