This paper deals with a probabilistic weighted multi-class support vector machines for face recognition. The support vector machines (SVM) has been applied to many application fields such as pattern recognition in last decade. The support vector machines determine the hyperplane which separates largest fraction of samples of the similar class on the same side. The SVM also maximizes the distance from the either class to the separating hyperplane. It has been observed that in many realistic applications, the achieved training data is frequently tainted by outliers and noises. Support vector machines are very sensitive to outliers and noises. It may happen that a number of points in the training dataset are misplaced from their true position or even on the wrong side of the feature space. The weighted support vector machines are designed to overcome the outlier sensitivity problem of the support vector machines. The main issue in the training of the weighted support vector machines algorithm is to build up a consistent weighting model which can imitate true noise distribution in the training dataset, i.e., reliable data points should have higher weights, and the outliers should have lower weights. Therefore, the weighted support vector machines are trained depending on the weights of the data points in the training set. In the proposed probabilistic weighted multi-class support vector machines the weights are generated by probabilistic method. The weighted multi-class support vector machines have been constructed using a combination of the weighted binary support vector machines and one-against-all decision strategies. Numerous experiments have been performed on the AR, CMU PIE and FERET face databases using different experimental strategies. The experimental results show that the performance of the probabilistic weighted multi-class support vector machines is superior to the multi-class support vector machines in terms of recognition rate.