This paper addresses the problem of network monitoring by proposing an Artificial Immune System (AIS) system to achieve situation recognition and monitoring in a large network of Wi-Fi hotspots as part of a highly scalable preemptive monitoring tool for wireless networks. Using a set of data extracted from a live network of Wi-Fi hotspots managed by an ISP, we integrated an AIS algorithm into a data collection system to detect anomalous performance and aberrant behavior in the ISP’s network. The results reveal the efficiency of the AIS system in terms of both anomaly performance and aberrant behavior on several test case scenarios.