The optimality of human performance when category base rates differ was investigated in 2 multidimensional perceptual categorization tasks. All participants were sensitive to differences in base rate, even during their 1st experimental session. Nearly half of the participants learned the optimal decision bound by their final experimental session. Little evidence for conservative cutoff placement was found (i.e., an underestimation of category base-rate differences). In fact, participants who did not learn the optimal decision bound tended to use a decision bound that overestimated the base-rate difference. Across all conditions participants showed a clear shift toward the optimal decision bound with experience. These data suggest that experienced participants are highly sensitive to differences in category base rate. The model-based analyses suggest that the decision-bound model of categorization (Ashby, 1992a; Ashby & Maddox, 1993; Maddox & Ashby, 1993) provides a powerful tool for investigating the limits of human categorization performance.