This paper describes an evaluation of a number of subsets of features for the purpose of image annotation using a non-parametric density estimation algorithm (described in). By applying some general recommendations from the literature and through evaluating a range of low-level visual feature configurations and subsets, we achieve an improvement in performance, measured by the mean average precision, from 0.2861 to 0.3800. We demonstrate the significant impact that the choice of visual or low-level features can have on an automatic image annotation system. There is often a large set of possible features that may be used and a corresponding large number of variables that can be configured or tuned for each feature in addition to other options for the annotation approach. Judicious and effective selection of features for image annotation is required to achieve the best performance with the least user design effort. We discuss the performance of the chosen feature subsets in comparison with previous results and propose some general recommendations observed from the work so far.