Several authors have proposed the use of statistical process control charting methods for the surveillance of endemic rates of nosocomial infections. The principal goal of such a charting program is to recognize any increase of the endemic rate to an epidemic rate as soon as possible after the change occurs. However, many of the statistical process control charting methods that have been proposed are based on classical charting principles that are effective largely for processes for which sufficient historical data are available. These methods require that a fairly large data set, taken while the infection rate was stable at a low endemic value, must be available to begin the charting process. These data are used both to confirm the appropriateness of the probability distribution and to make a control chart for the infection process based on the distribution. However, such data sets are often not available. The purpose of this article is to inform and demonstrate to readers that recent research in statistics has developed modern statistical process control methods that can be used effectively with or without such prior data. These methods make possible much more effective nosocomial infection surveillance programs that will give timely warnings of the onsets of epidemics or evidence of the effectiveness of infection control initiatives. These warnings will permit earlier correction initiatives and thus avoid much liability.