A Monte Carlo study compared the usefulness of six variable weighting methods for cluster analysis. Data were 100 bivariate observations from 2 subgroups, generated according to a finite normal mixture model. Subgroup size, within-group correlation, within-group variance, and distance between subgroup centroids were manipulated. Of the clustering methods examined, the flexible average algorithm with beta equal to -.15 or -.20 gave the best recovery. Of the remaining methods, that of J. H. Ward yielded the best recovery, followed closely by beta-flexible linkage and the EML algorithm of the Statistical Analysis System. In the absence of variable weights, negative within-group correlation resulted in much poorer recovery for all clustering algorithms. The ACE weighing method of D. Art, R. Gnanadesikan, and J. R. Kettenring proved preferable overall. Clustering with Mahalanobis distance based on the pooled within-group covariance matrix indicated that knowing the correct covariance method would yield improved recovery over the ACE method approximately 10% of the time. Two appendix figures provide weighting data. (Contains 8 figures, 7 tables, 2 appendix figures, and 40 references.) (Author/SLD)