The objective of this paper is to develop an artificial neural network (ANN) model which can be used to predict daily mean ambient temperatures in Denizli. south-western Turkey. In order to train the model, temperature values, measured by The Turkish State Meteorological Service over three years (2003-2005) were used as training data and the values of 2006 were used as testing data. In order to determine the optimal network architecture, various network architectures were designed; different training algorithms were used; the number of neuron and hidden layer and transfer functions in the hidden layer/output layer were changed. The predictions were performed by taking different number of hidden layer neurons between 3 and 30. The best result was obtained when the number of the neurons is 6. The selected ANN model of a multi-layer consists of 3 inputs, 6 hidden neurons and 1 output. Training of the network was performed by using Levenberg-Marquardt (LM) feed-forward backpropagation algorithms. A computer program was performed under Matlab 6.5 software. For each network, fraction of variance (R(2)) and root-mean squared error (RMSE) values were calculated and compared. The results show that the ANN approach is a reliable model for ambient temperature prediction. (c) 2008 Elsevier Ltd. All rights reserved.