Rice ( Oriza sativa , L.), as an important world food, is one of the most valuable crops with a long history of cultivation. Drying operation is used to prevent deterioration of such a strategic product. Since the quality of product is directly affected by the drying operation, controlling of this process is vitally important. Common methods to study the affecting factors on the drying process of agricultural products are statistical and mathematical models which mostly encounter several simplifications influencing the accuracy of the model. These methods hand in several differential and/or algebraically equations which must be solved and interpreted. However, if the problem involves several input and output variables (as in drying process), these methods are very complex to use. Nowadays, artificial intelligence technology as a success of rapid computer technology introduces artificial neural networks (ANN) for solving models for systems and processes problems. An ANN is a set of computational elements which are connected to each other like biological neurons and is able to communicate information without any previous knowledge of their relations. In this study, the ANN and multivariate regression methods were used to predict some affecting parameters on rough rice drying operation in a deep bed mode. The input parameters for these models were inlet air velocity (0.5, 0.8 and 1.1 m s -1 ), inlet air temperature (40, 50, 60, 70 and 80 °C) and inlet air relative humidity (40, 50, 60 and 70%). In addition to drying kinetic, three dependent variables including product output rate as the dryer capacity index, evaporation rate as the drying kinetic quality and kernel cracking percentage as the dried product quality were investigated. To create neural networks training, test and evaluation patterns, drying experiments were performed under different conditions by using a laboratory dryer. Then the results of the experiments were used in the design of neural networks. About 70 percent of the experiments were used for training the network and the rest for its test and evaluation. To predict the dependent parameters, three well - known networks namely multi-layer perceptron (MLP), generalized feed forward (GFF), and modular neural network (MNN) were examined. Drying kinetic was predicted using a network with four mentioned inputs. Other three parameters were firstly predicted with three separate A and then a single ANN was designed to predict all these three parameters simultaneously. Sensitivity analysis process gives valuable information about the model sensitivity to the input variables. sensitivity analysis was done according to Hill’s method. Results showed that the separate networks were more accurate than the Keywords : rough rice, drying kinetics, process and product parameters, artificial neural network