The drying operation is the most energy consuming step of particle board manufacturing process. Even if a great academic and industrial effort has been furnished for last years, the prediction of this energy consumption is still a challenging issue. This paper deals with the energy consumption prediction for industrial wood drying. The study of an European particle board manufacturer's industrial dryers has provided data sets for two both fresh and recycled wood drying processes. Based on these, MLP Neural network models have been developed and tested. Several tests have been conduced to identify and select the best MLP model's structure to find a satisfying trade-off between model accuracy and maintenance efficiency. The proposed MLP models have either been distinctly trained on the datasets from both the first and second dryers, and then on their combination, in order to increase data diversity and to reduce training time and model maintenance. Then, the neural network based on the merged dataset has been compared to those developed from the single datasets. This experiment led to the conclusion that, the construction of a global model representing the operation of the two dryers is less accurate than the construction of a dedicated model for each dryer. Yet, the performances of combination model remain acceptable.