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This paper describes an approach for automatic inspection of solder joints on printed circuit boards using gray-scale images. Common defects in solder joints are recognized using features computed from segmented solder joint subimages. Unacceptable joints are assigned to one of several defective classes. Defect classification, rather than just detection of defective joints, is motivated by the desire to automatically take corrective action on the assembly line. The features used for classification are based on characteristics of intensity surfaces. It is shown that features derived from surface facets are effective in the classification of solder joints using a minimum-distance classification algorithm.