Puri and Sen (1969b) introduced a nonparametric test statistic that, because of its relationship to the general linear model, subsumes many commonly performed hypothesis tests. Following the work of Puri and Sen, Harwell and Serlin (1985) proposed a test of the nonparametric analysis of covariance hypothesis. In order to use this statistic, ranks (or some other transformation) are substituted for the original scores. Standard statistical packages can then be used to perform the analysis, and the results of the test are referred to the appropriate reference distribution. The similarity of the rank transformation of Conover and Iman (1981) to this procedure is noted, and the results of a Monte Carlo study investigating the distributional properties (i.e., Type I error rate and power) of the proposed test and other nonparametric analyses of covariance models are presented.