Human facial expression recognition (FER) is a challenging research problem due to the feature extraction factor, which is an essential module for the FER system. In the case of facial expressions, the feature extraction factor means getting the distinguishable feature for each expression as a discrete symbol. Nowadays, modern artificial intelligent systems help to emulate and gauge the reactions of the face. The detection and recognition with any machine learning needs suitable training algorithms and then testing the data. This project aims to identify the human emotions from an image and classify them into one of the existing classes. The purpose of FER system is to implement the face detection algorithm presented by ViolaJones and try to detect all visible faces in every image. Then, the system crops the face and recognizes the emotion displayed by comparing it with the stored classes in the dataset. This research work employs a Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) features to classify seven expressions: anger, fear, disgust, happiness, sadness, neutral expression, and surprise. The system will determine the efficiency of the extracted features. The feature selection process is performed by (ReliefF) algorithm. In the given thesis, facial expression classification process is performed by Backpropagation Neural Network classifier. Better recognition results are achieved with a hybrid system of HOG and LBP, where the dimensionality is reduced up to thirty features instead of using thousands of features. This reduction increases the recognition systems' speed and removes redundant information.