It is widely believed that different clinical domains use their own sublanguage in clinical notes, complicating natural language processing, but this has never been demonstrated on a broad selection of note types. Starting from formal sublanguage theory, we constructed a feature space based on vocabulary and semantic types used in 17 different clinical domains by three author types (physicians, nurses, and social workers) in both the in- and outpatient settings. We supplied the resulting vectors to CLUTO, a robust clustering tool suitable for this high-dimensional space. Our results confirm that note types with a broad clinical scope, e.g, History & Physicals and Discharge Summaries, cluster together, while note types with a narrow clinical scope form surprisingly pure, disjoint sublanguages. A reasonable conclusion from this study is that any tool relying on term statistics or semantics trained on one clinical note type may not work well on any other.