In a multilingual world, information available in global information systems is increasing rapidly. Searching for proper names in foreign language becomes an important task in multilingual search and knowledge discovery. However, these names are the most difficult to handle because they are often unknown words that cannot be found in a translation dictionary and even human experts cannot handle the variation generated during translation. Furthermore, existing research on name translation have focused on translation algorithms. However, user experience during name translation and name search are often ignored. With the Web technology moving towards Web 2.0, creating a platform that allow easier distributed collaboration and information sharing, we seek methods to incorporate Web 2.0 technologies into a name translation system. In this research, we review challenges in name translation and propose an interactive name translation and search system: NameTran. This system takes English names and translates them into Chinese using a combined hybrid Hidden Markov Model-based (HMM-based) transliteration approach and a web mining approach. Evaluation results showed that web mining consistently boosted the performance of a pure HMM approach. Our system achieved top-1 accuracy of 0.64 and top-8 accuracy of 0.96. To cope with changing popularity and variation in name translations, we demonstrated the feasibility of allowing users to rank translations and the new ranking serves as feedback to the original trained HMM model. We believe that such user input will significantly improve system usability.