This paper shows the ability of artificial neural network (ANN) technology for predicting the correlation between farinographic properties of wheat flour dough and its chemical composition. The input parameters of the neural networks (NN) were the four most important chemical parameters influencing farinographic properties, namely protein content, wet gluten, sedimentation value and falling number. The output parameters of the NN models were six farinographic properties including water absorption, dough development time, dough stability time, degree of dough softening after 10 and 20 min and valorimeteric value. Results showed that, the Multi Layer ANN with training algorithm of back propagation (BP) was the best one for creation of non-linear mapping between input and output parameters. The ANN model predicted the farinographic properties of wheat flour dough with average RMS 10.794. These results show that the ANN can potentially be used to estimate farinographic parameters of dough from chemical composition. This development may have significant potential to improve product quality and reduce time and costs by minimizing farinographical experiments. (c) 2007 Elsevier Ltd. All rights reserved.