In this research, several models were developed to forecast the daily mean indoor temperature (IT) and relative humidity values in an education building in Izmir, Turkey. The city is located at a hot–humid climatic region. In order to forecast the IT and internal relative humidity (IRH) parameters in the building, a number of artificial neural networks (ANN) models were trained and tested with a dataset including outdoor climatic conditions, day of year and indoor thermal comfort parameters. The indoor thermal comfort parameters, namely, IT and IRH values between 6 June and 21 September 2009 were collected via HOBO data logger. Fraction of variance (R2) and root-mean squared error values calculated by the use of the outputs of different ANN architectures were compared. Moreover, several multiple regression models were developed to question their performance in comparison with those of ANNs. The results showed that an ANN model trained with inconsiderable amount of data was successful in the prediction of IT and IRH parameters in education buildings. It should be emphasized that this model can be benefited in the prediction of indoor thermal comfort conditions, energy requirements, and heating, ventilating and air conditioning system size.