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The main objective of this study is to improve allocation of water using real time climatic data to estimate irrigation requirement. A study was conducted on an irrigation network in Northwest of Iran to compare present water allocation technique, calculated based on traditional practice of using long term climatic data, and proposed practice of using real time data with the actual water allocation determined based on definite climatic data of the growing season. In this study, Neural Network techniques were used to estimate potential evapotranspiration (ET), net crop water use, and water allocation requirements. For predicting evapotranspiration in the subsequent 10-day period, ET data for one, two, three previous 10-day periods were used. The results of each Neural Network technique were analyzed and compared separately with actual and calculated ET. In regard to ET prediction, the results showed that focused time-delay method is more efficient than feed-forward, both in 10-day period and in monthly scales. In addition, better estimation can be obtained if climatic data from three preceding 10-day periods are used. Overall, incorporating new techniques resulted in 10 to 25 percent savings on water allocation within the network.