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The exhaustive quality control is becoming very important in the world́s globalised market. One example where quality control becomes critical is the percussion cap mass production, an element assembled in firearm ammunition. These elements must achieve a minimum tolerance deviation in their fabrication. This study outlines a machine vision system development using a three-dimensional camera for the inspection of the whole production of percussion caps. This system presents multiple problems, such as metallic reflections in the percussion caps, high-speed movement for scanning the pieces, and mechanical errors and irregularities in percussion cap placement. Owing to these problems, it is impossible to solve the problem using traditional image processing methods, and hence, machine-learning algorithms have been tested to provide a feasible classification of the possible errors present in the percussion caps.