Accurate estimation of potential evaporation has been of a great as its importance is obvious in many water resources applications such as management of hydrologic, hydraulic and agricultural systems. Although there are empirical formulas available for Evaporation estimation, but their performances are not all satisfactory due to the complicated nature of the evaporation process and the data availability. For this purpose, artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were developed to forecast monthly potential evaporation in Pantagar, U.S. Nagar (India) based on four explanatory climatic factors. Observations of relative humidity, solar radiation, temperature, wind speed and evaporation for the past 19 years and 8 months (total 236 months) have been used to train and test the developed models. Results revealed that the models were able to well learn the events they were trained to recognize. Moreover, they were capable of effectively generalizing their training by predicting evaporation for sets of unseen cases. These encouraging results were supported by high values of coefficient of correlation and low mean square errors. It has been found that ANN and ANFIS techniques have good performances (for the test data set, correlation coefficient for ANN is 0.9236 and root mean square error is 0.9863 and for ANFIS correlation coefficient is 0.9562 and root mean square error is 1.2812. Between ANN and ANFIS, ANFIS model is slightly better albeit the difference is small. Although ANN and ANFIS techniques seem to be powerful, their data input selection process was done by trial and error method