The concept of fuzzy time series is introduced and used to forecast the enrollment of a university. Fuzzy time series, an aspect of fuzzy set theory, forecasts enrollment using a first-order time-invariant model. To evaluate the model, the conventional linear regression technique is applied and the predicted values obtained are compared to the fuzzy time series results and actual enrollments. The forecasting procedure begins with "fuzzylising" the universe on which the historical data are based, and then interprets output results (actually fuzzy data sets). Comparison with linear regression shows the superiority of the fuzzy time series for forecasting enrollment. Three tables give comparison figures for fuzzy time series, linear regression, and actual values largely for 1972 through 1990. Three references are listed. (SLD)