SkyServer, the Internet portal for the Sloan Digital Sky Survey (SDSS) astronomic catalog, provides a set of tools that allows data access for astronomers and scientific education. One of SkyServer data access interfaces allows users to enter ad-hoc SQL statements to query the catalog. SkyServer also presents some template queries that can be used as basis for more complex queries. This interface has logged over 330 million queries submitted since 2001. It is expected that analysis of this data can be used to investigate usage patterns, identify potential new classes of queries, find similar queries, etc. and to shed some light on how users interact with the Sloan Digital Sky Survey data and how scientists have adopted the new paradigm of e-Science, which could in turn lead to enhancements on the user interfaces and experience in general. In this paper we review some approaches to SQL query mining, apply the traditional techniques used in the literature and present lessons learned, namely, that the general text mining approach for feature extraction and clustering does not seem to be adequate for this type of data, and, most importantly, we find that this type of analysis can result in very different queries being clustered together.