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Evaporation is one of the most essential parts of hydrological cycle, and is used to determine crop water requirement. Estimation of evaporation is useful for planning water management. In this study, we presented two estimations of evaporation using Multiple Regression and Box-Jenkins Methods. Monthly average climatic data consisting of pan evaporation, air temperature, atmospheric pressure, relative humidity, rainfall amount, and sunshine from Sakon Nakhon meteorological station were used in this study. The data were of 84 monthly records starting from January 2007 to December 2013. The first 76 records were used for constructing models by the stepwise multiple linear regression (MLR) and Box-Jenkins (BJ) methods, and the second 8 records were used for computing the accuracy of the models. Mean Absolute Deviation (MAD) and Mean Square Error (MSE) were evaluated to compare the accuracy of models. The result of stepwise MLR showed that the temperature and the relative humidity accounted for 78.3% of the variation of pan evaporation. The Box-Jenkins method can specify many appropriate ARIMA models, but the model ARIMA(0,1,1)(2,1,0)₁₂ (without a constant) was the most appropriate one due to its minimum AIC and SBC. Comparing the values MAD and MSE of both models, the MLR model was found to be more efficient than the ARIMA model for estimating monthly pan evaporation. Actually, the data of monthly pan evaporations are time series data with seasonal components; therefore, the effect of outliers should be included in the process of constructing the MLR model in order to increase the efficiency of the model.