Based on sample data representing five years of monthly circulation totals from 50 academic libraries in Illinois, Iowa, Michigan, Minnesota, Missouri, and Ohio, a study was conducted to determine the most efficient smoothing forecasting methods for academic libraries. Smoothing forecasting methods were chosen because they have been characterized as easy to use and fairly accurate. It was found that smoothing forecasting methods worked very poorly on monthly library data due to the seasonality present in monthly library circulation totals. The only method recommended for use with monthly data was Winters' Linear and Seasonal Exponential Smoothing method, which has a specific seasonal component. Much greater success was achieved by using smoothing forecasting methods with yearly-lagged data, for example, using the circulation totals of past Januarys to predict the total of a future January. The One-Month Single Moving Average was found to be the most efficient smoothing method for forecasting future monthly circulation totals on yearly-lagged data with litle or no trend, while Brown's One-Parameter Linear Exponential method (with alpha set at 0.5) was recommended for use in trending yearly-lagged data. These methods ranked first and second respectively in minimizing both the mean percentage forecasting error and standard deviation of forecasting errors. A 27-item bibliography and plots showing the circulation data from the 50 libraries are included. (ESR)