Nearest neighbour search (NNS) is an old problem that is of practical importance in a number of fields. It involves finding, for a given point q, called the query, one or more points from a given set of points that are nearest to the query q. Since the initial inception of the problem a great number of algorithms and techniques have been proposed for its solution. However, it remains the case that many of the proposed algorithms have not been compared against each other on a wide variety of datasets. This research attempts to fill this gap to some extent by presenting a detailed empirical comparison of three prominent data structures for exact NNS: KD-Trees, Metric Trees, and Cover Trees. Our results suggest that there is generally little gain in using Metric Trees or Cover Trees instead of KD-Trees for the standard NNS problem.