Canonical correlation analysis is a multivariate statistical model which facilitates the study of interrelationships among multiple dependent variables and multiple independent variables. It identifies components of one set of variables that are most highly related linearly to the components of the other set of variables. The underlying logic of canonical correlation analysis involves the derivation of a linear conbination of variables from each of the two sets of variables so that correlation between the two sets is maximized. Few research studies that use canonical correlation are reported in the literature because of: (1) prohibitive calculations prior to the use of computers; (2) limited awareness of canonical methods; (3) a multitude of mathematical symbolism used in discussions of the technique in textbooks; and (4) difficulty in interpreting canonical results. Greater use of the technique will be facilitated as computer packages become more readily available and the technique becomes more familiar. An illustration of the technique examines the relationship between the academic comfort and introversion/extraversion scores composite with the composite of the six interest areas of the Strong Vocational Interest Inventory. (LMO)