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The possibility of automating mass screening of gastric disease was studied by applying statistical pattern recognition methods to features extracted from standing position-anteroposterior radiograms by two approaches. A region of interest was defined to encompass the deformity of the apex or the sulcus angularis. The ability of the features to classify normal, ulcerated, and cancerous cases was evaluated by a typical method of discriminant analysis, the minimum Mahalanobis distance classification. The error classification rate was estimated by the leaving-one-out method. Best feature sets were selected by the forward sequential search. The separability of the samples and the properties of the features were inspected also by cluster analysis. For the selected feature sets, the error classification rate obtained by the discriminant analysis agreed well with results of the cluster analysis and of inspection by physicians. The selected features tended to belong to different feature clusters. This seems to validate the feature selection.