Abstract. In this paper we present three methods for image autoannotation used by the Wroclaw University of Technology group at ImageCLEF 2010 Photo Annotation track. All of our experiments focus on robustness of the global color and texture image features in connection with different similarity measures. To annotate training set we use two version of PATSI algorithm which searches for the most similar images and transferring annotations from them to the target image by applying transfer function. We use both the simple version of the algorithm working only on single similarity matrix, as well as multi-PATSI which uses many similarity measures in order to obtain the final annotations. As third approach to image auto-annotation we use Penalized Discriminant Analysis to train multi class classifier in One-vs-All manner. During training and optimization process of all annotators we use F-measure as evaluation measure trying to achieve its highest value on a training set. Obtained results indicate that our approach achieved a high quality measure only for a small group of terms and it is necessary to take into account also local image characteristics. 1