Wire electrical discharge turning (WEDT) is an emerging area, and it can be used to generate cylindrical forms on difficult to machine materials by adding a rotary axes to WEDM. The selection of optimum cutting parameters in WEDT is an important step to achieve high productivity while making sure that there is no wire breakage. In the present work, the WEDT process is modelled using an artificial neural network with feed-forward back-propagation algorithm and using adaptive neuro-fuzzy inference system. The experiments were designed based on Taguchi design of experiments to train the neural network and to test its performance. The process is optimized considering the two output process parameters, material removal rate, and surface roughness, which are important for increasing the productivity and quality of the products. Since the output parameters are conflicting in nature, a multi-objective optimization method based on non-dominated sorting genetic algorithm-II is used to optimize the process. A pareto-optimal front leading to the set of optimal solutions for material removal rate and surface roughness is obtained using the proposed algorithms. The results are verified with experiments, and it is found to improve the performance of WEDT process. Using this set of solutions, required input parameters can be selected to achieve higher material removal rate and good surface finish. © Springer-Verlag London 2012.