Many applications capture, or make use of, spatial data that changes over time. This requirement for effective and efficient spatio-temporal data management has given rise to a range of research activities relating to spatio-temporal data management. Such work has sought to understand, for example, the requirements of different categories of application, and the modelling facilities that are most effective for these applications. However, at present, there are few systems with fully integrated support for spatio-temporal data, and thus developers must often construct custom solutions for their applications. Developers of both bespoke solutions and of generic spatio-temporal platforms will often need to support the fusion of large spatio-temporal data sets. Supporting such requests in a database setting involves the use of join operations with both spatial and temporal conditions - spatio-temporal joins. However, there has been little work to date on spatio-temporal join algorithms or their evaluation. This paper presents an evaluation of several approaches to the implementation of spatio-temporal joins that build upon widely available indexing techniques. The evaluation explores how several algorithms perform for databases with different spatial and temporal characteristics, with a view to helping developers of generic infrastructures or custom solutions in the selection and development of appropriate spatio-temporal join strategies. © Blackwell Publishing Ltd. 2005.