Evaporation is a major meteorological component of the hydrologic cycle, and it plays an influential role in the development and management of water resources. The aim of this study is to predict of the monthly pan evaporation in Homs meteostation using Artificial Neural Networks (ANNs), which based on monthly air temperature and relative humidity data only as inputs, and monthly pan evaporation as output of the network. The network was trained and verified using a back-propagation algorithm with different learning methods, number of processing elements in the hidden layer(s), and the number of hidden layers. Results shown good ability of (2-10-1) ANN to predict of monthly pan evaporation with total correlation coefficient equals 96.786 % and root mean square error equals 24.52 mm/month for the total data set. This study recommends using the artificial neural networks approach to identify the most effective parameters to predict evaporation.