For multiple multivariate datasets, we derive conditions under which Generalized Canon- ical Correlation Analysis improves classification performance of the projected datasets, compared to standard Canonical Correlation Analysis using only two data sets. We illus- trate our theoretical results with simulations and a real data experiment.