Neural Network (NN) modeling techniques were used to predict flowability behavior in distillers dried grains with solubles (DDGS) prepared with varying CDS (10, 15, and 20%, wb), drying temperature (100, 200, and 300°C), cooling temperature (-12, 0, and 35°C) and cooling time (0 and 1 month) levels. Response variables were selected based on our previous research results, and included aerated bulk density, Hausner Ratio, Angle of Repose, Total Flowability Index, and Jenike Flow Function. Various neural network models were developed using multiple input variables in order to predict single response variables or multiple response variables simultaneously. The NN models were compared based on R 2 , mean square error (MSE), and coefficient of variation (% CV) obtained. In order to achieve results with higher R 2 and lower error, the number of neurons in each hidden layer, step size, momentum learning rate, and number of hidden layers were varied. Results indicate that for all the response variables, R 2 >0.83 was obtained from NN modeling. NN modeling provided better models than PLS modeling procedures (Bhadra et al., 2010c). Also, the best NN models fitted fairly well (R 2 >0.63) with the dataset of Ganesan et al (2007), indicating higher robustness in the proposed NN models. Finally, based on the predicted values (from NN modeling) surface plots yielded process and storage conditions for favorable vs. cohesive flow behavior in DDGS. Modeling of DDGS flowability using NN has not been previously done, and hence this work will be a step towards application of intelligent modeling procedures to this industrial challenge.