In this day and age, more and more people are using Online meeting tools to do meetings from the comfort of their office. However, the quality of these meetings as perceived by the users still requires some work, in order to make the experience comparable with the live one. So, policies for network management that favour Quality of Experience (QoE) of users are needed ever-more. The thesis aims to move the first step towards QoE defining for the first time the concept of user experience applied to this field, exploiting the potential of the assets based on machine learning. To do this, we explore the Real-time traffic (RTP) that is generated by meeting software and classify the different traffic flows – audio, video etc.- quality, through several classification algorithms.