A study was conducted to test the efficacy of detecting particulate contaminants in processed meat samples by visual observation of line-scanned x-ray images. Six hundred field-collected processedproduct samples were scanned at 230 cm2/s using 0.5 X 0.5-mm resolution and 50 kV, 13 mA excitation. The x-ray images were image corrected, digitally stored, and inspected off-line, using interactive image enhancement. Forty percent of the samples were spiked with added contaminants to establish the visual recognition of contaminants as a function of sample thickness (1 to 10 cm), texture of the x-ray image (smooth/ textured), spike composition (wood/bone/glass), size (0.1 to 0.4 cm), and shape (splinter/round). The results were analyzed using a maximum likelihood logistic regression method. In packages less than 6 cm thick, 0.2-cm-thick bone chips were easily recognized, 0.1-cm glass splinters were recognized with some difficulty, while 0.4-cm-thick wood was generally missed. Operational feasibility in a time-constrained setting was confirmed. One half percent of the samples arriving from the field contained bone slivers >1 cm long, 1/2% contained metallic material, while 4% contained particulates exceeding 0.3 cm in size. All of the latter appeared to be bone fragments.