Accent is probably the most prominent part in prosodic events. Automatic accent labeling is important for both speech synthesis and automatic speech understanding. However, manually labeling data for traditional supervised learning is expensive and time consuming. In this paper, we propose an unsupervised learning algorithm to label accent automatically. First, we assume all content words are accented. We build an initial acoustic model with accented vowels in content words and high confidence unaccented vowels in function words. Then an iterative progress is executed to convergence. Experimental results show that this unsupervised learning algorithm achieves about 90% agreement on accent labeling. Compared with 84.3%, the accuracy of a typical linguistic classifier, a 30% relative error reduction is obtained.