This paper introduces a new method to detect and track Skype traffic and users by exploiting cross layer information available within 3G mobile cellular networks. In a 3G core network all flows can be analyzed on a per user basis. A detected Skype message is therefore related to a specific user. This information enables user profiles that provide a relationship between the mobile station and the characteristics of the corresponding Skype instance, which remain unchanged for long periods of time. Based on this information, our computationally lightweight method is able to classify Skype flows accurately. Moreover, the method is, by design, robust against false positives. Based on test traces from a live network, our new method achieves a similar detection performance as publicly available tools, yet with much less complexity.