Machining conditions such as speed, feed, and depth of cut significantly affect tool wear, which in turn affects the surface quality and thus this is an area of research interest. With the growing emphasis on industrial automation in manufacturing, vision techniques play an important role in many applications. One of these applications is texture analysis. Although this technique has been extensively researched it has only infrequently been used to predict the cutting conditions of machined surfaces. This paper introduces an application of computer vision to predict the cutting conditions in milling operations (feed, speed, and depth of cut) using grey-level co-occurrence matrix texture features. A software, named the Cutting Condi- tions Prediction in Milling has been developed in order to predict the cutting conditions from the captured images of machined surfaces. Three modules were developed to perform the prediction process and they are presented in this paper. The system was verified by predicting cutting conditions for various specimens and the maximum error between the predicted and the actual cutting conditions did not exceed – 10.6 per cent.