Quantitative left ventriculography has been studied for many years as a method for assessing left ventricular function. All currently used quantitative left ventriculography methods evaluate functional status based on different assumptions concerning the time course of contraction, shape of the ventricular silhouette, and/or fixed points of reference. Commonly, analyses are performed using only end-diastolic (ED) and end-systolic (ES) frames. However, myocardial infarction manifests itself not only in spatial parameters, e.g., area of affected myocardium, but also in temporal parameters, e.g., deviation from a synchronous contraction during systole. ED/ES comparisons omit information contained in other frames of the cardiac cycle. Therefore, each single method has a limited reliability or usefulness. The present study used several quantitative methods, fitted curvatures, correlation coefficients of the percentage of radial shortening from end diastole to end systole, and Fourier coefficients of the polar coordinates of the endocardial contour points. Features were derived from a sequence of ventriculograms of a whole cardiac cycle from patients with anterior myocardial infarction (AMI group) and without (NV group) to address three major goals: (1) Determine features derived from LV-grams that discriminate between the two patient groups and do so with the highest accuracy, "recognition rate," or lowest error rates; (2) compare the accuracy of features generated by different quantitative methods; and (3) group features generated by the different quantitative methods to achieve higher accuracy. The discriminant capabilities of several feature sets were tested on a patient sample containing 102 patients (63 patients with AMI, 39 patients with no infarction) using numerical classification. The numerical classification had a Supervised Learning design with two cardiologists assigning the patients to the two patient groups. Each quantification method showed a characteristic pattern for detecting pathologic or normal wall motion. Combining features generated by different extraction methods into one feature set improved the correct classification rates.