Selecting an optimal web service among a list of functionally equivalent web services still remains a challenging issue. For Internet services, the presence of low-performance servers, high latency or overall poor service quality can translate into lost sales, user frustration, and customers lost. In this paper, we propose a novel method for QoS metrification based on Hidden Markov Models (HMM), which further suggests an optimal path for the execution of user requests. The technique we show can be used to measure and predict the behavior of Web Services in terms of response time, and can thus be used to rank services quantitatively rather than just qualitatively. We demonstrate the feasibility and usefulness of our methodology by drawing experiments on real world data. The results have shown how our proposed method can help the user to automatically select the most reliable Web Service taking into account several metrics, among them, system predictability and response time variability. Later ROC curve shows a 12 percent improvement in prediction accuracy using HMM.