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This paper deals with the prediction of flank wear of the cutting tool insert and surface roughness of the machined surface in hard turning using artificial intelligence technique. During machining of hard materials, the flank wear and surface roughness are favoured by both machining parameters and geometrical parameters. Prediction of output responses is much more important since surface roughness produced depends on the wear occurring at the flank face of the cutting tool insert. Based on the chosen input control parameters an L₁₈ orthogonal array is chosen for seven input parameters varied through three levels to perform experiments using Taguchi’s design of experiments. An artificial neural network (ANN) model is developed to predict the flank wear and surface roughness, which is compared with the predicted values from the developed empirical equations using multiple linear regression models. The …