A linear classification rule (used with equal covariance matrices) was contrasted with a quadratic rule (used with unequal covariance matrices) for accuracy of internal and external classification. The comparisons were made for seven situations which resulted from combining three data conditions (equal and unequal covariance matrices, minimal and nonminimal group centroid separation, and two and three criterion groups) for different sets of data. For the internal analysis the quadratic rule was superior in all seven situations. For the external analysis the linear rule was superior in nearly all of the situations. (Author)