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Samples from two mixed lots of tall fescue (Festuca arundinacea) and ryegrass (Lolium perenne) seed were hand separated and 10 physical properties of the individual seeds of both species were measured or calculated using a machine vision system (MVS). Data from measurements were subjected to logistic regression analysis, and models were created to distinguish the two species. A model created from the combined data from both lots characterized 83.13% of the tall fescue and 79.87% of the ryegrass. The combined regression model from both lots was then incorporated into a MVS algorithm, and previously unseparated quantities from each lot were separated based on the model. Using this procedure, an average of 83.87% of the tall fescue and 72.64% of the ryegrass seed were correctly classified. This procedure demonstrates potential for future automated MVS purity analysis and separation of mixtures of tall fescue and ryegrass and other difficult-to-separate seed lots.