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Vegetation classifications are a valuable tool for guiding management decisions. Vegetation can be classified at different levels of resolution for different purposes. Aggregating finer‐resolution classification units to simplify planning and decision‐making is becoming more common in land management. However, little is known about which resolution or scale of aggregation is appropriate for specific land management practices and the factors to consider when aggregating vegetation classification units. There is a risk that aggregated classification units may overlook vital floristic and structural differences between communities and not accurately represent the patterns of variation in vegetation. This may lead to unanticipated adverse outcomes from management actions. We compared three classification systems to test how well each represented patterns of variation in the vegetation of a semi‐arid region in Victoria, Australia: (a) a finer‐level classification system; (b) a coarser‐resolution system that aggregated finer compositional resolution units for fire management purposes; and (c) a coarser‐resolution classification based on Climate Zones. We conducted floristic and vegetation structure surveys at 249 sites stratified across a climate gradient. Sites were assigned to vegetation classifications based on mapped data layers and checked by ground truthing prior to inclusion. The classification based on a finer compositional resolution best represented variation in both the floristic and structural patterns of the vegetation across this region. This was not unexpected, because accuracy is traded‐off against simplicity when aggregating vegetation classification units. However, the aggregated classification also performed poorly in representing patterns of vegetation when compared with a simple scheme based on Climate Zones. Assessing the adequacy of aggregated vegetation classification units is important, yet often overlooked. To be effective, aggregated vegetation classes must reflect the underlying factors that drive patterns of variation in the vegetation to ensure they support intended management outcomes. [ABSTRACT FROM AUTHOR]