Monitoring patients in intensive care units is a critical task. Simple condition detection is generally insufficient to diagnose a patient and may generate many false alarms to the clinician operator. Deeper knowledge is needed to discriminate among the flow of alarms those that necessitate urgent therapeutic action. Overall, it is important to take into account the monitoring context: sensor and signal context (are the signal data noisy or clear?), the patient condition (is the state of the patient evolving or stable? is the condition of the patient critical or safe?), the environmental context (do the external conditions influence the patient condition or not?). To achieve the best surveillance as possible, we propose an intelligent monitoring system that makes use of several artificial intelligence techniques: artificial neural networks for signal processing and temporal abstraction, temporal reasoning, model based diagnosis, decision rule based system for adaptivity and machine learning for knowledge acquisition. To tackle the difficulty of taking context change into account, we introduce a pilot aiming at adapting the system behavior by reconfiguring or tuning the parameters of the system modules. A prototype has been implemented and is currently experimented and evaluated. Some results, showing the benefits of the approach, are given.