Abstract In this study, pedotransfer functions (PTFs) were developed for estimating the gravimetric water content based on the model of Dexter et al. (2008) using feed-forward artificial neural networks. Soil samples were collected from 148 locations in the West Azarbaijan, Hamedan, Fars, and Kurdistan provinces of Iran. The cation exchange capacity (CEC), organic matter content, electrical conductivity and equivalent CaCO3, bulk density, penetration resistance (PR) curve, soil water retention curve (SWRC), and particle size distributions of the soils were measured. Various PR models were fitted to the experimental PR data and the model parameters were then used to estimate the SWRC with nine versions via the model of Dexter et al. Among the two PR models that described the PR versus the water content, the parameters of the model proposed by Mielke et al. (1994) obtained more accurate PTFs and improved the water content estimates. In addition, using the parameters in the model of Stock and Downes (2008) based on suction and organic matter improved the water content estimates. Measuring the PR and water content is cheaper and requires less time than measuring the PR and matric suction, so we recommend using the parameters in model of Mielke et al. (1994) as water content predictors.