In this study, artificial neural network (ANN) model was developed for predicting draft and energy requirements of a disk plow. The ANN model utilizes ten input parameters: plowing depth and speed, sand content, silt content, clay content, soil moisture content, disk diameter and angle, tilt angle and soil density. The model provides the draft, unit draft and energy requirements of disk plows as the predicted output. The ANN model was trained based on data fromliterature and tested on actual data from field experiments. The architecture of the ANN model consisted of one hidden layer with 8 nodes. Standard backpropagation-based algorithm was used to train the network. The results showed that correlation coefficients for testing points were 0.934, 0.933 and 0.915 for draft, unit draft and energy requirements, respectively. The promising results obtained indicate that the newly developed ANN model can be considered as a practical and reliable tool for predicting disk plow performance criteria under wide range of conditions. Using the network weights obtained from the ANN model, new formulations were presented for the calculation of draft, unit draft and energy requirements. Furthermore, as an added benefit, these formulations can be implemented with any programming language or spreadsheet program making them an attractive choice for routine analyses.