Due to the mechanical properties and machinability of AISI 1040 steel, it has a broad range of industrial applicability. The present work investigates the dependence of the average temperature generated at the tool work interface on machining parameters such as cutting speed, feed rate and depth of cut. The machining parameters were varied at three equally spaced levels. Experiments were carried out following combinations in Taguchi's L 27 orthogonal array. A simple and cost-effective tool work thermocouple was devised and calibrated to obtain the tool work interface temperature. A 3-5-1 feed forward artificial neural network (ANN) model trained using the Levenberg-Marquardt (LM) back propagation algorithm resulted in efficient modelling of the complex relationship between the average cutting temperature and machining parameters (R-value of 0.99). The trained network was optimized using genetic algorithm (GA) to predict optimal turning parameters. This methodology has been termed as ANN-GA method. A significant reduction in the average cutting temperature could be realized due to optimization using ANN-GA method. Analysis of variance revealed highest contribution from the cutting speed and its square term in controlling the cutting temperature at tool work interface.