<p>This paper reports on the effect of cutting parameters viz. cutting speed, feed rate, depth of cut on surface roughness, tool wear, and vibration during machining of high-carbon chrome steel (EN 31). Experimentation on machining was performed with a PVD-coated carbide tool using Taguchi L27 orthogonal array, and the data results were analyzed via analysis of variance (ANOVA), regression modeling, and the predictions made through Artificial Neural Network-based one. The difference in feeds was the greatest determinant of the surface roughness, vibration and tool wear. The next impact was the feed rate followed by the change in the depth cut that had little impact. Increased surface roughness (by up to 40%) and the development of Built-Up Edge were greatly decreased by the use of high cutting speeds, leading to chip removal being chip evacuation of smoother quality. Increased feed rates and depths of cut caused increased levels of wear on the tool and unwanted roughness created by high levels of cutting force and plastic deformation. The Artificial Neural Network model presented a high predictive value compared to regression models whose R-value exceeded 0.98 in capturing the nonlinear functions between the parameters of machining parameters and outputs. SEM analysis revealed that the process was dominated by abrasion, adhesion and diffusion wear mechanism. It was also observed that the abrasive wear was extremely sharp whenever feed rate and depth of cut were high. The use of minimum quantity lubrication (MQL) to extend tool life and machining, advanced tool coating, and maximizing the cutting speed should be recommended.</p> Graphical abstract <p></p>