This paper reviews approaches for automated pattern spotting and knowledge discovery in spatially referenced data. This is an emerging field which to date has received developmental contributions primarily from researchers in statistics and knowledge discovery in databases (KDD). The field of geographical information systems (GIS) has, however, recognized its importance as a means for providing more exploratory analysis functionality. Tools based upon automated approaches that identify potentially important relationships in spatial data are essential in GIS in order to effectively deal with the increasing amounts of information being gathered. Clustering techniques are proving to be valuable, but there appears to be a general lack of understanding associated with the use and application of various clustering methods in the geographic domain. Further, there is little if any recognition of the relationships between clustering methods. As a result, the development of techniques known to be problematic or inferior has occurred. This paper presents an overview of clustering methods for exploratory spatial data analysis and associated application issues.