Accurate characterization of repeated measures mixed models depends on assumptions about the correlation of observations across measurement periods. Adjacent time periods are likely more similar than distant time periods, but how is that similarity captured? The correlation structure of a repeated measures model answers that question. From the perspective of model implementation, the more structures available, the better the chance of approximating the way data looks in reality. Prior to JMP Pro 13 only three commonly used time based repeated measures covariance structures were available: unstructured, autoregressive (with a period of 1), and residual. This made characterization challenging, leaving a large number of potential solutions unavailable to the modeler. JMP Pro 13 introduces seven new structures, greatly expanding the modeling opportunities. This talk will discuss modeling repeated measures data using any of the existing covariance structures including those traditionally reserved for spatial relationships. It will provide examples illustrating their relationship to each other, when to use which structure, and how to compare them to find the best fit. A brief overview of repeated measures mixed models will be given, as well as the SAS code corresponding to the examples.