The methods of statistical pattern recognition are well suited to the problems ofin vivoultrasonic tissue characterization. This paper describes supervised pattern recognition methods for selecting features for tissue classification, calculating decision boundaries within the selected feature space, and evaluating the performance. This paper expands on the authors' previous work (Insana, 1986a) by addressing the considerations of dimensionality and sample size. These are important in classification problems where the underlying probability distributions are not completely known. Examples are given for the detection of diffuse liver disease in the clinical environment.