In this paper we analysed the use of a hybrid CSTR/neural network model to describe the highly coupled heat and mass transfer during paste drying with inert particles in vibrofluidized beds. The model was developed to predict the air temperature and relative humidity at the outlet of dryer throughout drying time, as well as the moisture content of powder collected in the cyclone. These variables were chosen as they are the most important to monitor the process efficiency and to develop strategies for process control. Drying was modelled based on the global energy and water mass balances with an inter-phase coupling term that takes into account both the water evaporation and particle coating, and was described by a fitted neural network (ANN) model. The proposed model was verified by comparing the predictions to experimental data obtained in drying calcium carbonate suspensions with different solids contents, skimmed milk and sewage sludge, at a vibration parameter equals to 4.0 and different amplitudes of vibration. The results showed that the ANN model was effective to estimate the inter-phase coupling term. The dynamics behavior of the outlet air temperature and relative humidity were well described by the CSTR lumped model for most conditions evaluated. (C) 2015 Elsevier B.V. All rights reserved.