Evapotranspiration forms one of the hydrology cycle elements that it's hard to measure its actual amounts in the field conditions, so it’s estimated by calculations of experimental relations that depend on climatic elements data. These estimations include different errors because of approximation processes. The research goals to accurate estimation of the monthly reference evapotranspiration amount in Safita area (on the east coast of the Mediterranean Sea), and the research depends on the technique of Artificial Neural Network (ANN), and the mathematical model was built by the (nftool), which is one of the Matlab tools, depending on monthly air temperature and relative humidity data which were taken from Safita meteorological station, and the data of monthly pan evaporation (Class A pan) has been used, after modifying its results, for the purpose of checking the performance accuracy of the network, by using Simulink technique, which is existing in Matlab Programs Package. The results of the research verify that a multi-layer ANN of error Back-propagation algorithm gives a good result in estimating monthly reference Evapo-transpiration for the used data group.