One task faced by public health surveillance practitioners is the timely identification of data patterns that might suggest the onset of an epidemic period. Many available techniques for analysis of surveillance data are based on sequential procedures, which predict expected numbers of cases and compare this estimate with observed values. To detect changes in the reported occurrence of a disease (increase, decrease, or change in trend), we used exponential smoothing and transformation of the difference between the observed and estimated data to calculate a function called the probability index. We illustrate this procedure using weekly provisional data for measles cases in the US reported through the National Notifiable Diseases Surveillance System to the Centers for Disease Control and Prevention (CDC). The method is potentially useful in public health surveillance to facilitate prompt intervention and prevention efforts, since it can be used at the national and regional levels without the requirement for sophisticated computing.