We have developed an early warning prototype, based on a knowledge management approach, so as to carry out the online detection of crises. Experts, with the help of automatic tools, design an ontology describing domain-specific crisis eruption processes. Then a recognition engine performs model-based inference, in order to identify among the events feeding the system, typical sequences that might trigger a crisis. Crises are described in the ontology through the template technique which provides also mechanisms to assess the similarity between stored scenarios and event flows related to the monitored process. This technique takes into account imperfect knowledge and uncertainties: for instance, imprecise temporal constraints between events are represented by fuzzy sets.