We present a robust shape model for localizing a set of feature points on a 2D image. Previous shape alignment models assume Gaussian observation noise and attempt to fit a regularized shape using all the observed data. However, such an assumption is vulnerable to gross feature detection errors resulted from partial occlusions or spurious background features. We address this problem by using a hypothesis-and-test approach. First, a Bayesian inference algorithm is developed to generate object shape and pose hypotheses from randomly sampled partial shapes - subsets of feature points. The hypotheses are then evaluated to find the one that minimizes the shape prediction error. The proposed model can effectively handle outliers and recover the object shape. We evaluate our approach on a challenging dataset which contains over 2,000 multi-view car images and spans a wide variety of types, lightings, background scenes, and partial occlusions. Experimental results demonstrate favorable improvements over previous methods on both accuracy and robustness.