Social networking websites have gained importance in recent years. In them, users can connect with other users to interact with. However, generally, the number of registered users is very large. Therefore, find other users with common interests is not easy. Recommender systems are software tools which generate suggestions for various types of items to users and can be applied to recommend people (other users) on social networks. Systems that recommend people use specific techniques and, due to the social implications involved in personal relationships, must take several factors into consideration. The lack of data available makes the task of generate good recommendations more difficult. This paper discusses the theme and presents a person recommendation system for social networking websites based in user connections. To test the system presented, we conducted an experiment with Peladeiro, a real website of a social network that has over 500 000 users, where few reliable data are available.