There is a great demand to assess video quality transmitted in real time over packet networks, and to make this assessment in real time too. Quality assessment is achieved using two types of methods: objective or subjective. Subjective methods give more reliable results than objective methods; the latter do not always correlate well with human perception. Unfortunately, subjective methods are not suitable to real-time applications and are very difficult to carry out. In this paper, we show how Artificial Neural Networks (ANN) can be used to mimic the way by which a group of human subjects assess video quality when this video is distorted by certain quality-affecting parameters (e.g. packet loss rate, loss distribution, bit rate, frame rate, encoded frame type, etc.). Our method can be used to measure in real time the subjective video quality with very good precision. In order to illustrate its applicability, we chose to assess the quality of video flows transmitted over IP networks and we carried out subjective quality tests for video distorted by variations of those parameters.