A Bayesian hierarchical model was used to study the spatial variation in mortality risk from lung and pleural cancer in both sexes, and breast and soft tissue sarcoma (STS) in women in Piedmont (north-west Italy, average population 4 349 411) from 1980 to 2000. Of these four neoplasms, two are common (lung and breast) and two rare (pleura and STS); two have well recognized risk factors (lung and pleura) while the other two (breast and STS) have no single strong risk factor. Data were analysed at a small-area level (1206 municipalities, population 39 to 989 663), using both standardized mortality ratios and Bayesian-estimated mortality risks. The Bayesian model allowed for both heterogeneity (through spatially independent random effects) and clustering (through spatially correlated random effects) and, by borrowing information from neighbouring areas, provided stable estimates for areas with sparse data. The aim was to reduce the noise in the disease maps to highlight the true underlying mortality distribution. Lung cancer in men showed strong spatial structure with a marked east-west gradient, but no appreciable urban-rural differences. In contrast, high mortality areas for female lung cancer were observed around conurbations. Female breast cancer and STS appeared to be spread uniformly across the region. Pleural cancer mortality clusters were evident around areas with major asbestos manufacturers, or natural asbestiform fibre pollution. Maps of Bayesian-estimated mortality risk provided appreciably clearer pictures of risk distribution than did maps of the standardized mortality ratio.