Automatic learning plays an important role in image analysis and pattern recognition. A taxonomy of automatic learning strategies is presented; this categorization is based on the amount of inferences the learning element must perform to bridge the gap between environmental and system knowledge representation level. Four main categories are identified and described: rote learning, learning by deduction, learning by induction, and learning by analogy. An application of learning by induction to medical image analysis is then exposed. It consists in the classification of two-dimensional gel electrophoretograms into meaningful distinct classes, as well in their conceptual description.