This paper addresses the issues surrounding an individual's exposure to potential environmental risk factors, which can be implicated in the aetiology of a disease. We hope to further elucidate the ‘lag’ or latency period between the initial exposure to potential pathogens and the physical emergence of the disease, with specific reference to the rare neurological condition, motor neurone disease (MND), using a dataset obtained from the Finnish Death Certificate registry, for MND deaths between the period 1985–1995. A space–time approach is adopted, whereby patterns in both time and space are considered. No prior assumptions about the aetiology of MND are adopted. By using methods for the analysis of point processes, which preserve the continuous nature of the data, we resolve some of the problems of analysis that are often based on arbitrary areal units, such as postcode boundaries, or political boundaries. We use kernel estimation to model space–time patterns. Raised relative risk is assessed by adopting appropriate adjustments for the underlying population at risk, with the use of controls. Significance of the results is assessed using Monte Carlo simulation, and comparisons are made with results obtained from Openshaw's geographical analysis machine (GAM). Our results demonstrate the utility of kernel estimation as a visualisation tool. Small areas of elevated risk are identified, which need to be more closely examined before any firm conclusions can be drawn. We highlight a number of issues concerning the inadequacies of the data, and possibly of the techniques themselves.