Image annotation is an important computer vision problem where the goal is to determine the relevance of annotation terms for images. Image annotation has two main applications: (i) proposing a list of relevant terms to users that want to assign indexing terms to images, and (ii) supporting keyword based search for images without indexing terms, using the relevance estimates to rank images. In this paper we present TagProp, a weighted nearest neighbour model that predicts the term relevance of images by taking a weighted sum of the annotations of the visually most similar images in an annotated training set. TagProp can use a collection of distance measures capturing different aspects of image content, such as local shape descriptors, and global colour histograms. It automatically finds the optimal combination of distances to define the visual neighbours of images that are most useful for annotation prediction. TagProp compensates for the varying frequencies of annotation terms using a term-specific sigmoid to scale the weighted nearest neighbour tag predictions. We evaluate different variants of TagProp with experiments on the MIR Flickr set, and compare with an approach that learns a separate SVM classifier for each annotation term. We also consider using Flickr tags to train our models, both as additional features and as training labels. We find the SVMs to work better when learning from the manual annotations, but TagProp to work better when learning from the Flickr tags. We also find that using the Flickr tags as a feature can significantly improve the performance of SVMs learned from manual annotations.