Based upon Ferguson's Dirichlet process, we introduce an order-statistic process, a technical device which may be viewed as defining a continuum of fractional order statistics for any sample size. The order-statistic process provides an alternative to the quantile function essentially based on stochastic rather than linear interpolation. Its use in large-sample theory is suggested, and a simple proof is given that the order-statistic process, suitably normalized, converges to a Gaussian process. Applications to small-sample theory and to the passage-time distribution of Yule processes are also considered, and a class of censored-data problems is related to mixtures of these processes.