This paper presents a constructive training algorithm for Multi Layer Perceptron (MLP) applied to facial expression recognition applications. The developed algorithm is composed by a single hidden-layer using a given number of neurons and a small number of training patterns. When the Mean Square Error MSE on the Training Data TD is not reduced to a predefined value, the number of hidden neurons grows during the neural network learning. Input patterns are trained incrementally until all patterns of TD are presented and learned. The proposed MLP constructive training algorithm seeks to find synthesis parameters as the number of patterns corresponding for subsets of each class to be presented initially in the training step, the initial number of hidden neurons, the number of iterations during the training step as well as the MSE predefined value. The suggested algorithm is developed in order to classify a facial expression. For the feature extraction stage, a biological vision-based facial description, namely Perceived Facial Images PFI has been applied to extract features from human face images. To evaluate, the proposed approach is tested on three databases which are the GEMEP FERA 2011, the Cohn-Kanade facial expression and the facial expression recognition FER-2013 databases. Compared to the fixed MLP architecture and the literature review, experimental results clearly demonstrate the efficiency of the proposed algorithm.