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Identifying chatter or intensive self-excited relative tool–workpiece vibration is one of the main challenges in the realization of automatic machining processes. Chatter is undesirable because it causes poor surface finish and machining accuracy, as well as reducing tool life. The identification of chatter is performed by evaluating the surface roughness of a turned workpiece undergoing chatter and chatter-free processes. In this paper, an image-processing approach for the identification of chatter vibration in a turning process was investigated. Chatter is identified by first establishing the correlation between the surface roughness and the level of vibration or chatter in the turning process. Images from chatter-free and chatter-rich turning processes are analyzed. Several quantification parameters are utilized to differentiate between chatter and chatter-free processes. The arithmetic average of gray level G …