The effects of drying conditions on rice moisture content removal rate have been extensively studied. The drying conditions consist of drying air temperature, drying air humidity, drying air flow rate, drying time per cycle, drying time, initial moisture content of grain, and the variety of rice. An alternative approach that determines the moisture content removal rate model using neural network technology is suggested in this article. The experimental drying conditions and moisture content removal rate are used as input and desired patterns to estimate the connection weights of neural network. In this paper, a three-layer feed-forward neural network and back-propagation learning algorithm are proposed for the modeling of rice drying. The generated neural network model can be used to analyze the effects of drying conditions on rice moisture content removal rate and predict the needed drying time for a desired final moisture content.