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During operation, buildings consume a large amount of energy, in developed countries around 40% of total final energy use. A major challenge is to reduce the amount of energy used while still providing a comfortable environment for building occupants. The use of passive techniques, such as natural ventilation, is promoted in certain climates to provide low energy cooling and ventilation. However, controlling natural ventilation in an effective manner to maintain occupant comfort can be a difficult task, particularly during warm periods. One area which has been identified as having the potential for reducing energy consumption while maintaining occupant comfort is the use of more advanced control techniques and a move towards “intelligent” buildings. A technique which has been much explored in recent years for application in mechanically ventilated buildings is Model Predictive Control (MPC). The essential component of an MPC strategy is the predictive model of the building's thermal dynamics. In this paper a data driven, neural network approach to system modelling is taken to model internal temperatures. Building data from a recently built naturally ventilated school and an office building are used to train multilayer perceptron neural network models and the resulting models performance are examined. The models developed were found to have good prediction capabilities over reasonable prediction horizons; however the effect of the control input was not captured.