This study uses an algorithm-based visual display technique (FACES) to provide enhanced detection of multivariate outliers within large-scale data sets. The FACES computer graphing algorithm (H. Chernoff, 1973) constructs a cartoon-like face, using up to 18 variables for each case. A major advantage of FACES is the ability to store and show the values of the variables. The research used data from a national, longitudinal study of school children, their parents, and their teachers, and the National Education Longitudinal Study of 1988 (NELS-88). Data for the Stanford Achievement tests from a Florida school district were also used. Four random stratified samples of 250 cases each were drawn from male and female databases for the NELS-88 and Stanford scores. Pearson product-moment correlation coefficients were calculated between the variables in each sample, and variables were assigned to the features of the FACE. The application of the FACES graphing algorithm translates the data into a meaningful visual correlate, providing one summative visual image per student. The ease of outlier detection on seeing a page of Chernoff FACES is readily apparent to the trained researcher, the school administrator, and parents. Four appendixes contain the Mahalanobis distance values for faces for the four samples. Also attached is a set of FACES for each sample that illustrates outlier cases. (SLD)