There is an alarming news recently revealed on media that 8.7 percent of users on Facebook are fake; this amounts to more than 83 million accounts worldwide. Consequently this huge number of fake users whose profiles were unverified translates to the potential dangers ranging from espionage, identity thievery, information misuse and loophole to privacy compromise to the users and their families. Nowadays with the popularity of online social networks (OSN), it is easy to footprint a potential target from the information easily trawled from the Web. Anyone can simply impose as somebody else that s/he claimed to be, without checking whether the information is genuine or not. For example it is so easy to impersonate one's identity on OSN by supplying fake photos and false names, which will go preemptively unchecked by Facebook. In this paper, a preliminary experiment of applying decision tree classification algorithms is presented, for identifying imposters from a pool of friends in Facebook. The classification approach is similar to that of classifying spams from legitimate emails except the attributes of a user's account is taken into consideration instead of text-mining the message contents. An accuracy of 92.1% is demonstrated to be achievable using the classification techniques.