In this paper, we propose a real-time hand gesture recognition model. This model is based on both a shallow feedforward neural network with 3 layers and an electromyography (EMG) of the forearm. The structure of the proposed model is composed of 5 modules: data acquisition using the commercial device Myo armband and a sliding window approach, preprocessing, automatic feature extraction, classification, and postprocessing. The proposed model has an accuracy of 90.1% at recognizing 5 categories of gestures (fist, wave-in, wave-out, open, and pinch), and an average time response of 11 ms in a personal computer. The main contributions of this work include (1) a hand gesture recognition model that responds quickly and with relative good accuracy, (2) an automatic method for feature extraction from time series of varying length, and (3) the code and the dataset used for this work, which are made publicly available.