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The classification of data sessions on the Internet is a crucial issue for Authorities involved in lawful interception. Some Internet Service Providers (ISP) can provide a panel of IP nodes that, tuned to detect specific data patterns, are able to send an alert when a data session in a targeted class is found. Unluckily, several applications generate a bulk of IP traffic not characterized by a recognizable sequence of information segments, except, may be, for some short phases such as setup and release. Whenever such phases are not intercepted, no specific pattern in the IP traffic can help toward semantic recognition and hence statistical pattern recognition is in force. This is actually the case of Skype, the popular application for VoIP communications. In this paper we propose and evaluate a decision theory based system allowing to recognize Skype traffic with the help of an open-source machine learning tool: Weka.