A novel method for the on-line identification of steady state in noisy processes is developed. The method uses critical values of an F-like statistic, and its computational efficiency and robustness to process noise distribution and non-noise patterns provide advantages over existing methods. The distribution of the statistic is obtained through Monte-Carlo simulations, and analytically derived shifts in the distribution due to process ramp changes and autocorrelations in the process data are shown to cross check with simulations. Application is demonstrated on experimentally measured pH, temperature and pressure data.