Recent papers by Rogers (1986) and Thompson et al. (1987) suggest fitting curves to annual age-specific fertility rates, forecasting the parameters of the curves using time series techniques, and then using the forecasted curves to generate forecasts of future age-specific fertility rates. This approach reduces the dimensionality of the forecasting problem, but the error in the fitted curves is not negligible. We present an approach based on a principal components approximation to the rates that avoids this problem to a large extent, while still reducing dimensionality. This approach is compared with direct univariate modeling of all the age-specific rates, and with the curve fitting approaches.