This paper presents a method for 3D Human Body pose estimation. 3D real data of the searched object is acquired by a multi-camera system and segmented by a special preprocessing algorithm based on clothing analysis. The human body model is built by nine SuperQuadrics (SQ) with a-priori known anthropometric scaling and shape parameters. The pose is estimated hierarchically by RANSAC-object search with a least square fitting 3D point cloud to SQ models: at first the body, and then the limbs. The solution is verified by evaluating the matching score, i.e. the number of inliers corresponding to a-piori chosen distance threshold, and comparing this score with admissible inlier threshold for the body and limbs. This method can be used for 3D object recognition, localization and pose estimation of Human Body.