This paper deals with the development of statistical methodology for timely detection of incident disease clusters in space and time. The increasing availability of data on both the time and the location of events enables the construction of multivariate surveillance techniques, which may enhance the ability to detect localized clusters of disease relative to the surveillance of the overall count of disease cases across the entire study region. We introduce the surveillance conditional predictive ordinate as a general Bayesian model-based surveillance technique that allows us to detect small areas of increased disease incidence when spatial data are available. To address the problem of multiple comparisons, we incorporate a common probability that each small area signals an alarm when no change in the risk pattern of disease takes place into the analysis. We investigate the performance of the proposed surveillance technique within the framework of Bayesian hierarchical Poisson models using a simulation study. Finally, we present a case study of salmonellosis in South Carolina.