Despite recent advancements in terms of network performance, end users still face slow web browsing situations, which can have a range of causes, such as a congested Wi-Fi, a bad wireless signal, or a loaded network or end host. It is thus crucial to monitor the network and troubleshoot the specific causes of slow web browsing, as this benefits end users, operators, and internet service providers alike. Various tools attempting to actively troubleshoot the network through the injection of probes exist. However, these tools are, on the one hand, expensive to run and, on the other hand, not general enough to be able to identify the specific cause of web browsing slowness. This paper addresses the problem by proposing a new lightweight passive measurement solution capable of transforming the web performance measurements collected from within the browser into indicators of network performance anomalies. We validate our solution by emulating a controlled network environment with manually injected anomalies; then, we leverage the measurement data available within the browser to build a predictive model that uses a random forest classifier to correctly classify the causes of web browsing performance degradation, with an accuracy of over 95%. This implies that one can build on our solution to propose a tool, in the form of a browser extension, that can be used in the wild to monitor the network and shed light on its anomalies by solely relying on a regular user’s web activity.