The field of monitoring drinking water acquires a particular importance in the last few years. The control of risks in the factories that produce and distribute water ensures the quality of this vital resource. Several methods and techniques were implemented in order to reduce these risks. We present here by a new technique called: Support Vector Machines (SVMs). This method is developed from the statistical learning theory, which displays optimal training performances and generalization in several fields, among others the field of pattern recognition. The exposed technique ensures within a monitoring system, a direct and quasi permanent quality control of water. For a validation of the performances of this technique used as classification tool, a study in simulation of the training time, the recognition rate and the noise sensitivity, is carried out. With an aim of showing its functionality, an application test is presented.
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