Manufacturing flaws of all types, shapes, and sizes can be exhaustively detected as abnormal pixels, if process and noise variations can be learned at every pixel in the inspection area. This statistical template approach to automated visual inspection is extremely fast, effective, and flexible, while achieving false negative rate <10/sup -6/. Critical to this approach are the following novel features: 1) represent both geometry and process information in a model template; 2) align 3D surfaces with subpixel accuracy; compensate for local deformation and texture; 4) estimate bimodal distribution robustly. This novel paradigm was applied to the automatic screening of X-ray images of turbine blades. It has been validated with over 50,000 images and shown to outperform regular inspectors looking at high-pass filtered images.