Data from graduate student applications at a large Western university were used to determine which factors were the best predictors of success in graduate school, as defined by cumulative graduate grade point average. Two statistical models were employed and compared: artificial neural networking and simultaneous multiple regression. Both models yielded similar results, indicating that the combination of the following factors could predict 10-12 percent of the variance in graduate grade point average: (1) college to which the student was applying; (2) marital status; (3) gender; (4) GRE verbal and analytical scores; and (5) residency region of students. (Contains 8 tables.)