Several methods of testing for multivariate trends have been discussed in the statistical and water quality literature. We review both parametric and nonparametric approaches and compare their performance using synthetic data. A new method, based on a robust estimation and testing approach suggested by Sen and Puri, performed very well for serially independent observations. A modified version of the covariance inversion approach presented by Dietz and Killeen also performed well for serially independent observations. For serially correlated observations, the covariance eigenvalue method suggested by Lettenmaier was the best performer.