Linear classification functions (LCFs) arise in a predictive discriminant analysis for the purpose of classifying experimental units into criterion groups. The relative contribution of the response variables to classification accuracy may be based on LCF-variable correlations for each group. It is proved that, if the raw response measures are "standardized," the ordering of the variables can be accomplished by ordering the means of the standardized measures within each group. A computational procedure is presented for obtaining these means as well as for obtaining the LCF-variable within-groups correlations. These correlations may be used for two purposes: (a) to characterize each group with respect to variable scores, and (b) to determine the relative contribution of the variables to separate group classification accuracy. It is proved that the LCF-variable correlations for a group are proportional to standardized variable means for that group. The proof enables a researcher to quite readily accomplish the two interpretation purposes. Two data sets are used to illustrate the interpretation of LCFs. (Author/RL)