Various methods for clustering mixed-mode data are compared. It is found that a method based on a finite mixture model in which the observed categorical variables are generated from underlying continuous variables out-performs more conventional methods when applied to artificially generated data. This method also performs best when applied to Fisher's iris data in which two of the variables are categorized by applying thresholds.