Several researchers around the world have studied gesture recognition, but most of the recent techniques fall in the curse of dimensionality and are not useful in real time environment. This study proposes a system for dynamic gesture recognition and prediction using an innovative feature extraction technique, called the Convexity Approach. The proposed method generates a smaller feature vector to describe the hand shape with a minimal amount of data. For dynamic gesture recognition and prediction, the system implements two independent modules based on Hidden Markov Models and Dynamic Time Warping. Two experiments, one for gesture recognition and another for prediction, are executed in two different datasets, the RPPDI Dynamic Gestures Dataset and the Cambridge Hand Data, and the results are showed and discussed. (C) 2016 Elsevier Inc. All rights reserved.