Proposes a method for generalized constrained canonical correlation analysis (GCCANO) that incorporates external information on both rows and columns of data matrices. In this method, each set of variables is first decomposed into the sum of several submatrices according to the external information, and then canonical correlation analysis is applied to pairs of derived submatrices. Demonstrates the usefulness of the method with two examples. (SLD)