Summary: I examine the self-consistency of a principal component axis; that is, when a distribution is centered about a principal component axis. A principal component axis of a random vector \(\mathbf X\) is self-consistent if each point on the axis corresponds to the mean of \(\mathbf X\) given that \(\mathbf X\) projects orthogonally onto that point. A large class of symmetric multivariate distributions is examined in terms of self-consistency of principal component subspaces. Elliptical distributions are characterized by the preservation of self-consistency of principal component axes after arbitrary linear transformations. A “lack-of-fit” test is proposed that tests for self-consistency of a principal axis. The test is applied to two real datasets.