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In this paper we evaluate several methods of tting data to conic sections. Conic tting is a commonly required task in machine vision, but many algorithms perform badly on incomplete or noisy data. We evaluate several algorithms under various noise and degeneracy conditions, identify the key parameters which a ect sensitivity, and present the results of comparative experiments which emphasize the algorithms' behaviours under common examples of degenerate data. In addition, complexity analyses in terms of op counts are provided in order to further inform the choice of algorithm for a speci c application.