Canonical　Correlation　Analysis　(CCA)　has　recently　attracted　great　attention　and　many　experimental　results　have　illustrated　its　effectiveness.　In　this　paper,　we　study　the　relationship　between　CCA　classifier　and　minimum　squared　error　(MSE)　classifier.　It　helps　us　look　into　the　nature　of　CCA　classifier.　In　traditional　CCA　method,　the　class-membership　matrix　is　deliberately　coded　in　full　rank.　Under　this　case,　we　will　prove　CCA　is　equivalent　to　MSE　classifier.　It　is　also　shown　that　even　the　class-membership　matrix　is　centered　and　thus　not　in　full　rank,　CCA　is　equivalent　to　Fisher　Linear　Discriminant　Analysis　(FDA).　Some　experiments　are　presented　to　verify　the　results.　©　2008　IEEE.