User profiles can serve as indicators of personal preferences which can be effectively used while providing personalized services. Building user files which can capture accurate information of individuals has been a daunting task. Several attempts have been made by researchers to extract information from different data sources to build user profiles on different application domains. Towards this end, in this paper we employ different classification algorithmsto create accurate user profiles based on information gathered from demographic data. The aim of this work is to analyze the performance of five most effective classification methods, namely Bayesian Network(BN), Na�ve Bayesian(NB), Naives Bayes Updateable(NBU), J48, and Decision Table(DT). Our simulation results show that, in general, the J48has the highest classification accuracy performance with the lowest error rate. On the other hand, it is found that Na�ve Bayesian and Naives Bayes Updateable classifiers have the lowest time requirement to build the classification model.