In two experiments subjects were required to make medical diagnoses for simulated patients on the basis of the symptoms the patients had. Each patient had one of two diseases, the common disease, which occurred on 75% of trials, or the rare disease, which occurred on 25%, and these diseases occurred with 16 different combinations of four symptoms. In the first experiment, subjects learned across a large number of trials to associate the symptoms with the diseases, and the probability of diagnosing each disease for each symptom pattern was recorded. These probabilities were well modelled by a connectionist network. In the second experiment, a stronger test of the connectionist model was attempted. The crucial feature was that the probability of the rare disease given one particular symptom was equal to the probability of the common disease given the same symptom, but the contingency between the symptom and the rare disease was greater than that between the symptom and the common disease, which meant that the symptom was a better predictor of the rare disease. Subjects in one group were more likely to diagnose the rare disease than the common disease on these trials than were subjects in a control group, thus showing a bias associated with the differing base-rates of the diseases. The bias is consistent with the predictions of the connectionist account of categorization. In fact, while the data could be comfortably accommodated by a connectionist theory, they are difficult to reconcile with a variety of alternative theories of categorization. Finally, some possible limitations of connectionist accounts of categorization are considered.