The purpose of this paper is to investigate the applicability of long‐term feature averaging as an eventual means for performing text independent voice authentication (speaker verification). Based upon a set of long‐term feature vectors, a principal component analysis is performed to obtain a normalized reference coordinate system for each speaker. Features extracted from the test speaker are transformed to this coordinate system and then the Euclidean distance is measured. It is shown thatunder a weak assumption of Gaussian statistics, the threshold necessary to attain a given probability of correct acceptance as a function of the number of dimensions or features can be theoretically calculated. Results of several preliminary experiments are presented to illustrate the technique.