Comparative evaluation of population recovery capabilities of 35 cluster analysis methods defined by different combinations of 5 profile similarity measures and 7 agglomeration rules was undertaken using artificial data that represented duplicate mixture samples from 4 latent populations. The latent population mean profiles differed primarily in elevation or in pattern parameters. Latent population sampling variances were controlled to provide two different levels of realistic overlap. The within-population distributions were multivariate normal with diagonal covariance structure. Across all conditions examined, complete linkage and Ward's minimum variance methods, used with Euclidian or city block interprofile distance measures, performed best. Single linkage, median, and centroid methods were substantially inferior for clustering individuals in accordance with true population memberships.