An electrocardiographic computer program based on multivariate analysis of orthogonal leads (Frank) was applied to records transmitted daily by telephone from the Veterans Administration Hospital, West Roxbury, Mass., to the Veterans Administration Hospital, Washington, D. C. A Bayesian classification procedure was used to compute probabilities for all diagnostic categories that might be encountered in a given record. Computer results were compared with interpretations of conventional 12 lead tracings. Of 1,663 records transmitted, 1,192 were selected for the study because the clinical diagnosis in these cases could be firmly established on the basis of independent, nonelectrocardiographic information. Twenty-one percent of the records were obtained from patients without evidence of cardiac disease and 79 percent from patients with various cardiovascular illnesses. Diagnostic electrocardiographic classifications were considered correct when in agreement with documented clinical diagnoses. Of the total sample of 1,192 recordings, 86 percent were classified correctly by computer as compared with 68 percent by conventional 12 lead electrocardiographic analysis. Improvement in diagnostic recognition by computer was most striking in patients with hypertensive cardiovascular disease or chronic obstructive lung disease. The multivariate classification scheme functioned most efficiently when a problem-oriented approach to diagnosis was simulated. This was accomplished by a simple method of adjusting prior probabilities according to the diagnostic problem under consideration.