The present work attends to diagnose and classify individual stress using online client server architecture where the case-based reasoning (CBR) and fuzzy logic methods are implemented in the server side. Recent studies show that stress may cause health problems, and several clinical studies state that it is possible to measure the stress level of a patient through their fingers temperature. Therefore, the main aim of this book is to determine individual stress level using a special hardware device which records the fingers temperature. Afterwards, the recorded data is sent to the server in order to be matched and compared with 39 sample cases stored in the case library, already diagnosed and classified by an expert clinician. CBR and fuzzy similarity return the closest cases according to a matching procedure which considers several features (temperature evolution, gender, room temperature...). Other available retrieving methods, like Euclidean distance or similarity matrix, have been exhaustively evaluated as well, fuzzy similarity revealing as the most accurate. Everything is gathered under an intuitive and very usable GUI, easily accessible for both patients and clinicians.