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With the expansion of digital networks and TV devices and the rapid increase of the number of channels, people are exposed to an information overload, due to the presence of several hundreds of alternative programs to watch. In this context, personalization is achieved with the employment of algorithms and data collection schemes that predict and recommend to television viewers content that match their interests and/or needs. This paper introduces queveo.tv: a personalized TV program recommendation system. The proposed hybrid approach (combining content-filtering techniques with those based on collaborative filtering) also provides all typical advantages of any social network as comments, tagging, ratings, etc. This web 2.0 application has been devised to enormously simplify the task of selecting what program to watch on TV.