Since growth curves are often used to produce medium- to long-term forecasts for planning purposes, it is obviously of value to be able to associate an interval with the forecast trend. The problems in producing prediction intervals are well described by Chatfield. The additional problems in this context are the intrinsic non-linearity of the estimation procedure and the requirement for a prediction region rather than a single interval. The approaches considered are a Taylor expansion of the variance of the forecast values, an examination of the joint density of the parameter estimates, and bootstrapping. The performance of the resultant intervals is examined using simulated data sets. Prediction intervals for real data are produced to demonstrate their practical value.