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This paper examines issues arising in applying a previously developed edit-distance shock graph matching technique to indexing into large shape databases. This approach compares the shock graph topology and attributes to produce a similarity metric, and results in 100% recognition rate in querying a database of approximately 200 shapes. However, indexing into a significantly larger database is faced with both the lack of a suitable database, and more significantly with the expense related to computing the metric. We have thus (i) gathered shapes from a variety of sources to create a database of over 1000 shapes from forty categories as a stage towards developing an approach for indexing into a much larger database; (ii) developed a coarse-scale approximate similarly measure which relies on the shock graph topology and a very coarse sampling of link attributes. We show that this is a good first …