This article presents a framework supporting rapid prototyping of multimodal applications, the creation and management of datasets and the quantitative evaluation of classification algorithms for the specific context of gesture recognition. A review of the available corpora for gesture recognition highlights their main features and characteristics. The central part of the article describes a novel method that facilitates the cumbersome task of corpora creation. The developed method supports automatic ground truthing of the data during the acquisition of subjects by enabling automatic labeling and temporal segmentation of gestures through scripted scenarios. The temporal errors generated by the proposed method are quantified and their impact on the performances of recognition algorithm are evaluated and discussed. The proposed solution offers an efficient approach to reduce the time required to ground truth corpora for natural gestures in the context of close human–computer interaction.