Feedback utterances are among the most fre- quent in dialogue. Feedback is also a crucial aspect of all linguistic theories that take social interaction involving language into account. However, determining communicative func- tions is a notoriously difficult task both for human interpreters and systems. It involves an interpretative process that integrates vari- ous sources of information. Existing work on communicative function classification comes from either dialogue act tagging where it is generally coarse grained concerning the feed- back phenomena or it is token-based and does not address the variety of forms that feed- back utterances can take. This paper intro- duces an annotation framework, the dataset and the related annotation campaign (involv- ing 7 raters to annotate nearly 6000 utter- ances). We present its evaluation not merely in terms of inter-rater agreement but also in terms of usability of the resulting reference dataset both from a linguistic research per- spective and from a more applicative view- point.