The Semantic Networking concept has been introduced to solve the QoS, scalability and complexity challenges for the Future of Internet. Based on traffic awareness and considering flow entities, it contributes to an adaptive management of the network and provides better knowledge of the transported traffic. Studying the processing time of the classification compatible with real-time operation of such networks is a key question for implementation purposes. In this paper, we present interesting techniques for classification of traffic in semantic networks. The Sample & Hold and multi-stage filter schemes are studied to detect the biggest flows. Their performance is evaluated on real traffic traces. In addition the classification of traffic according to the originating application is investigated. In particular, we analyze the influence of many parameters derived from a traffic flow on the performance of application identification and classify them according to their accuracy. By doing this, a light scheme is proposed able to classify accurately the traffic. We finally discuss the architecture of an hardware implementation to validate the concept of semantic networking.