This paper, evaluates the influence of depth information on the gesture recognition process. We propose depth silhouettes, a natural extension of the binary silhouette concept, as a mechanism to incorporate depth information for gesture recognition. Using depth silhouettes, we define extensions of three classic techniques employed previously for gesture recognition with monocular vision. These include: (a) silhouette compression using PCA and learning with HMM; (b) an exemplar-based gesture recognition using HMM; and (c) temporal templates that in this work are compressed using PCA and learned with SVM. The results obtained show that, independently of the technique employed, the use of depth silhouettes increases the success significantly. Additionally, we show how the best results are obtained through the combined use of PCA and HMM.