In this project we looked at the problem of location recognition in a large image dataset. This involved finding the location of a query image in a large geotagged dataset containing 30,000 streetside images of a city. We investigate the performance of the vocabulary tree approach as the size of the database grows. In particular, we show that by carefully selecting the vocabulary using only the most informative features, retrieval performance is significantly improved. We also present a generalisation of the standard vocabulary tree search algorithm which improves performance by considering several likely paths.