This paper discusses ho the conventional similarity measure orks on the practical medical data set. The similarity measure used as linear combination of the Mahalanobis distance between numerical attributes and the Hamming distance between nominal attributes. We performed clustering experiments on the meningoencephalitis data set using the similarity measure in conjunction with four types of clustering algorithms:single-and complete-linkage agglomerative hierarchical clustering, Ward?s method and rough clustering. Usefulness of the similarity measure as evaluated from the following viewpoints: (1) quality of the generated clusters, (2) clinical reasonability of the attributes used to generate the high-quality clusters. The results showed that the best clusters were obtained using Ward?s method where the clinically reasonable attributes were selected. It suggests that this similarity measures would be applicable to the medical data sets.