Online shopping has been accepted by more and more consumers. C2C websites provide thousands of offers for consumers as a mainstream e-commerce platform. When customers search products in C2C website, some returned offers have misleading description. Misleading description means that the description does not convey the actual price of products, but usually claiming much lower price for the purpose of attracting more consumers. The misleading offers affect consumers' judgments and bring bad influences on the websites' reputation. This paper proposes an approach that combines statistical model HMM with statistical outlier detection method to detect misleading offers. HMM model is built to determine the product that an offer description really designates, providing an efficient solution to eliminate the ambiguity of the offer description caused by description irregularities. The statistical outlier detection method is effective to deal with limited product offer information. The paper further conducts experiments on real data set of electric business websites and the results demonstrate the effectiveness of the proposed approach.