In this paper, a robust 3D dance posture recognition system using two cameras is proposed. A pair of wide-baseline video cameras with approximately orthogonal looking directions is used to reduce pose recognition ambiguities. Silhouettes extracted from these two views are represented using Gaussian mixture models (GMM) and used as features for recognition. Relevance vector machine (RVM) is deployed for robust pose recognition. The proposed system is trained using synthesized silhouettes created using animation software and motion capture data. The experimental results on synthetic and real images illustrate that the proposed approach can recognize 3D postures effectively. In addition, the system is easy to set up without any need of precise camera calibration.