Maps are an important source of information in archaeology and other sciences. Users want to search for historical maps to determine recorded history of the political geography of regions at different eras, to find out where exactly archaeological artifacts were discovered, etc. Currently, they have to use a generic search engine and add the term map along with other keywords to search for maps. This crude method will generate a significant number of false positives that the user will need to cull through to get the desired results. To reduce their manual effort, we propose an automatic map identification, indexing, and retrieval system that enables users to search and retrieve maps appearing in a large corpus of digital documents using simple keyword queries. We identify features that can help in distinguishing maps from other figures in digital documents and show how a Support-Vector-Machine-based classifier can be used to identify maps. We propose map-level-metadata e.g., captions, references to the maps in text, etc. and document-level metadata, e.g., title, abstract, citations, how recent the publication is, etc. and show how they can be automatically extracted and indexed. Our novel ranking algorithm weights different metadata fields differently and also uses the document-level metadata to help rank retrieved maps. Empirical evaluations show which features should be selected and which metadata fields should be weighted more. We also demonstrate improved retrieval results in comparison to adaptations of existing methods for map retrieval. Our map search engine has been deployed in an online map-search system that is part of the Blind-Review digital library system.