Most previous studies on named entity recognition (NER) focus on common names such as persons,organizations,and locations in formal texts.With the development of e-commerce and online advertising,how to recognize product names which are special named entities in informal users context becomes more and more important.We design a maximum entropy model to recognize product names from forum posts and explore the impact of various features on the performance.These features include not only traditional features used for NER,but also distributed word representations which are novel ones obtained from the new area of machine learning.We compare the results of the experiments using different feature combinations as inputs.Experiments on the CPROD01 dataset show that the Brown cluster features can improve the accuracy of the product name recognition system.