Abstract Accurately predicting pile shaft resistance when designing pile foundations is necessary for ensuring appropriate structural and serviceability performance. The scope of this research includes four main components: (I) compiling shaft resistance datasets obtained from the published literature; (II) developing two artificial neural network (ANN) and non-linear multi regression models for predicting pile shaft resistance using cone penetration test (CPT) results; (III) investigating the influence of input parameters on the resulting shaft friction and their degrees of importance; and (IV) assessing the relative accuracies of the presented models using a number of traditional methods. It is quantitatively demonstrated that the ANN and non-linear multiple regression models proposed in the current study out perform the traditional methods and can be used by engineers to accurately predict pile shaft resistance.