In automatic speaker recognition methods, the speaker to be recognized is usually required to speak the same utterance which was used to obtain the reference pattern for that speaker. However, such a restriction is not generally necessary for speaker recognition by humans. Is reliable automatic speaker recognition similarly possible? A speaker identification experiment was performed in a population of 10 female speakers. The acoustical information was represented by sets of 12 predictor coefficients obtained by minimizing the mean-squared prediction error over speech segments 50 msec in duration. Each set of these predictor coefficients was represented by a 12-dimensional vector. New sets of coordinates which minimized the intraspeaker variance were determined by linear transformation of the original vector space. The segment of the utterance used to identify an individual was different from the segments used to form the reference pattern for that individual. For each segment, the unknown vector was correlated with the reference vectors and the correlations were averaged over a number of segments—the speaker with the largest correlation was identified as the unknown speaker. The over-all identification accuracy was 93% for 40 speech segments. These results suggest that successful automatic speaker identification is possible independently of the spoken text.