Spring back compensation is essential for accurate geometry of sheet metal components. In this paper the effect of process parameters namely sheet thickness, bend angle and tool travel rate on spring back in SS304 and C80 material sheets under V-bending is predicted by using finite element method and artificial neural network approaches. Total nine experiments were designed considering three process parameters, each with three levels, by using Taguchi`s L9 orthogonal array. The results obtained by ANN model are in good agreement with FEM model. This establish the robustness of ANN model for predicting spring back value and may be used an alternative to FEM model as the latter is more expensive and time consuming. The optimized value of sheet thickness, bend angle and tool travel rate are 2 mm, 80°and 6 mm ms -1 respectively for SS 304 material and 2 mm, 80°and 2 mm ms -1 for C80 material.