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An automated system drills the outer moldline holes on a military aircraft wing. Currently, the operator manually checks countersink diameter every ten holes as a process quality check. The manual method of countersink inspection (using a countersink gauge with a dial readout) is prone to errors both in measurement and transcription, and is time consuming since the operator must stop the automated equipment before measuring the hole. Machine vision provides a fast, non-contact method for measuring countersink diameter, however, data from machine vision systems is frequently corrupted by non-gaussian noise which causes traditional model fitting methods, such as least squares, to fail miserably. We present a solution for circle measurement using a statistically robust fitting technique that does an exceptional job of identifying the countersink even in the presence of large amounts of structured and non-structured noise such as tear-out, scratches, surface defects, salt-and-pepper, etc.