Discriminant analysis (DA) encompasses procedures for classifying observations into groups (i.e., predictive discriminative analysis) and describing the relative importance of variables for distinguishing amongst groups (i.e., descriptive discriminative analysis). In recent years, a number of developments have occurred in DA procedures for the analysis of data from repeated measures designs. Specifically, DA procedures have been developed for repeated measures data characterized by missing observations and/or unbalanced measurement occasions, as well as high-dimensional data in which measurements are collected repeatedly on two or more variables. This paper reviews the literature on DA procedures for univariate and multivariate repeated measures data, focusing on covariance pattern and linear mixed-effects models. A numeric example illustrates their implementation using SAS software.