In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery these valuable mineral, a gravity concentrator viz. wet shaking table was used.Optimisation along with performance prediction of the unit operation is necessary for efficient recovery.So, in this present study, an artificial neural network(ANN) modeling approach was attempted for predicting the performance of wet shaking table in terms of grade(%) and recovery(%). A three layer feed forward neural network(3:3–11–2:2) was developed by varying the major operating parameters such as wash water flow rate(L/min), deck tilt angle(degree) and slurry feed rate(L/h). The predicted value obtained by the neural network model shows excellent agreement with the experimental values. In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery of valuable mineral, a gravity concentrator viz. Wet shaking table was used. Optimization along with performance prediction of the unit operation is necessary for efficient recovery.So, in this present study, an artificial neural network (ANN) modeling approach was attempted for predicting the performance of wet shaking table in terms of grade (%) and recovery (%). A three layer feed forward neural network : 3-11-2: 2) was developed by varying the major operating parameters such as wash water flow rate (L / min), deck tilt angle (degree) and slurry feed rate (L / h). The predicted value obtained by the neural network model shows excellent agreement with the experimental values.