A 2-D object recognition algorithm applicable for partial occluded object recognition is proposed. The boundary of object of interest is extracted first. Then we segment the boundary into curve segments using dominant points, followed by a proportional extension. Normalization is then performed for each segment to make them translation, orientation and scaling invariant. After that, each segment is represented by its wavelet descriptors at multi-scale. A hierarchical iterative matching is performed to identify the object from low to high resolution. Experiment result shows proposed recognition algorithm is robust to similarity transform, noise and occlusion, and it is computational efficient.