The conventional approach for testing the equality of two normal mean vectors is to test first the equality of covariance matrices, and if the equality assumption is tenable, then use the two-sample Hotelling T[superscript 2] test. Otherwise one can use one of the approximate tests for the multivariate Behrens-Fisher problem. In this article, we study the properties of the Hotelling T[superscript 2] test, the conventional approach, and one of the best approximate invariant tests (Krishnamoorthy & Yu, 2004) for the Behrens-Fisher problem. Our simulation studies indicated that the conventional approach often leads to inflated Type I error rates. The approximate test not only controls Type I error rates very satisfactorily when covariance matrices were arbitrary but was also comparable with the T[superscript 2] test when covariance matrices were equal.