Hand posture recognition remains an active topic in computer vision and human robot interaction. Althrough there exists numerous methods for this, the main question " What is the best features for hand representation " seems to be not resolved yet. In this paper, we propose to investigate the role of a new descriptor, named kernel descriptor (KDES), recently introduced in 1 for hand posture recognition. As the hand posture has it own the color characteristic, we will examine kernel descriptor in di ffi dent color channels such as HSV, RGB, Lab to find out the most suitable color space for kernel representation of hand posture. To evaluate the performance of the proposed method, we perform extensive experiments on two datasets that show a very promising result (97% on NUS-2 dataset and 85% on our dataset). Thank to the analysis, kernel descriptor is highly recommended for hand posture recognition.