Principal component analysis is a technique often found to be useful for id
entifying structure in multivariate data. Although it has various character
izations (Rao 1964), the most familiar is as a variance-maximizing projecti
on. Projection pursuit is a methodology for selecting low-dimensional proje
ctions of multivariate data by the optimization of some index of "interesti
ngness" over all projection directions. Principal component analysis can be
viewed as an example of projection pursuit, and we justify its success in
structure identification by characterizing it in terms of maximum likelihoo
d under the assumption of normality.