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.