The aim of parametric classification is to predict the target class of a new sample, under the hypothesis of known fitted distribution. A major drawback of this approach is the uncertainty due to the imprecise modeling of the training samples. For this purpose, a belief functions framework is provided to take into account uncertainties. The proposed method investigates the belief functions theory to assign a confidence weight to each class for any new sample. This approach yields a confidence-weighted parametric classification method for multi-class problems. The performance of the proposed method is validated by experiments on real data for indoor localization and for facial image recognition.