We developed a test statistic based on an approach of Whittemore et al. (1987) to detect space-time clustering for non-infectious diseases. We extended the spatial test of Whittemore et al. by deriving conditional probabilities for Poisson distributed random variables. To combine spatial and time distances we defined a distance matrix D, where dij is the distance between the ith and jth cell in a three-dimensional space-time grid. Spatial and temporal components are controlled by a weight. By altering the weight, both marginal tests and the intermediate test can be reached. Allowing a continuum from a pure spatial to a pure temporal test, the best result will be gained by trying different weights, because the occurrence of a disease might show some temporal and some spatial tendency to cluster. We examined the behaviour of the test statistic by simulating different distributions for cases and the population. The test was applied to the incidence data of insulin-dependent diabetes mellitus in Finland. This test could be used in the analysis of data which are localized according to map co-ordinates, by addresses or postcodes. This information is important when using the Geographical Information System (GIS) technology to compute the pairwise distances needed for the proposed test.