Pressure drop is one of the most common problems that occur during waterflooding. In this study, pressure drop due to calcium sulfate scaling in Berea sandstone was predicted by multi-layer perceptron and radial basis function neural networks. To design the optimum multi-layer perceptron model, number of neurons, number of hidden layers, and training function were studied. Also, the spread parameter and normalization method of data were examined for radial basis function model. The multi-layer perceptron model, with two hidden layers, predicted pressure drop with an average error of 1.19%, whereas error of the radial basis function model was 1.76%.