This program provides two alternative Bayes solutions to problems of classifying an individual into one of K mutually exclusive populations on the basis of measurements taken on p predictor variables. It is assumed that the individual must have come from one of the K populations and must be assigned to one of them. Two simplifying assumptions are made. First, the p measurements are assumed to have a multivariate normal distribution in each of the populations.' Secondly, all misclassification errors are considered equally costly. The Bayes decision rule minimizes the total probability of misclassification. In this procedure. an individual is classified by means of one for each of the K populations, resulting in the assignment of the individual to that population for which he has the largest posterior probability. Such a Bayes procedure requires the probabilities that an individual, drawn at random, belongs to a given population. This procedure does not, however, require that the covariance matrices of the K populations be equal (homogeneous); a test of the homogeneity assumption is made by the program. If they are equal, the discriminant scores can be reduced to functions of the predictor variables. They are therefore called linear discriminant scores. When the covariance matrices are unequal, the discriminant scores are functions and are called quadratic discriminant scores. Thus, the mathematical form of the discriminant scores differentiates two types of Bayee procedures-linear and quadratic-both of which are provided by the program. For detailed discussions, see Anderson (1958), Rao (1965), and Fu1comer (1970). Method. (1) Notation. The following terms are used in this program description: K = number of populations, p = number of variables, x' = (xj , ', Xp ) = vector of predictor scores, 1Tk = prior probability of the kt h population, JJ.k = mean vector of the k t h population, ~k = covariance matrix of the kt h population, rii =cost in assigning an individual who actually belongs to the it h population to the jth, Pi(X)=probability density at Xfor the it h population, § = sample space of all potential observations, Wi = classification region for the it h popula tion. Carets (e .g., Pk) indicate the use of sample estimates for corresponding parameters. (2) Decision Rule. The expected loss in applying a decision rule for an individual from the it h population is