In this paper a data based mechanistic (DBM) model is proposed using a simplified heat balance formulation for modelling the temperature distribution inside a full scale ventilated room. The model has a number of parameters which are physically meaningful and determined using time temperature data obtained from experiments for several inlet air flow rates. At the inlet a step input in air temperature is applied and temperature responses at 36 sensor locations were recorded. For all ventilation rates used, the parameters of the model are extracted using statistical identification technique. Later, model based predictive control (MBPC) algorithm is developed to control temperature profiles on pre-selected sensor locations. The developed DBM model is compact in structure and found to capture the temperature distribution with high accuracy. The MBPC which is distinguished by explicit use of process models, is robust for disturbance and noise effects. Besides it has high tracking capability of the reference trajectory. (c) 2004 Elsevier B.V. All rights reserved.