Geophysical inversion is often ill-posed because of its nonlinearity and the ordinary measured data of measured data. To deal with these problems, an artificial neural network (ANN) has been introduced with the capability of a nonlinear and complex problem for geophysical inversion. This study aims to invert 2D cross-hole electrical resistivity tomography data using a feedforward back-propagation neural network (FBNN) approach. To generate the synthetic data to train the model, eighteen forward models (100 to 600 Ω.m homogeneous medium and three different locations of 10 Ω.m of the grouted bulb) with a dipole-dipole array configuration were adopted. The effect of the hyperparameter on the performance of the proposed FBNN model was examined. Various datasets from the laboratory testing result were also tested using the suggested FBNN model and then the error between the actual and predicted area in each model was determined. The results show that our suggested FBNN model, with the trainrp training function, 4 hidden layers, 75 neurons in each hidden layer, 0.8 learning rate, 1 of momentum coefficient, and 54,000 training data points, has higher performance and better accuracy than other models. It was found that the error value of the FBNN model was about 15% to 18% lower compared to the conventional inversion model.