In this article we are interested in group decision aiding for an ordinal classification problem. Our approach is based on a multiagent system where each decision maker is represented by a user agent and the process is guided by a mediator agent. Each user agent has a personalized preferencebased behavior defined by a utility function. The aim of the process is to converge to a group classification using a negotiation procedure. We present an experiment with real and simulated data in order to illustrate our approach and assess its performance (with respect to both user satisfaction and privacy) comparing it with two centralized methods.