Continuous and real-time gesture spotting is a key factor for the development of novel Human-Robot Interaction (HRI) modalities and further push the use of robots in our society. In this paper we present a hand gesture recognition module for large vocabularies of static and dynamic gestures, with limited training. The recognition module uses feature-samples obtained with an automatic motion detection-based segmentation algorithm, being the source data obtained from a magnetic tracker for the wrist and a data glove for the hand. The classifiers proposed are Multi-Layer Neural Networks (Perceptrons) (MLP) with one or two hidden-layers, with an accuracy of 98.7% for 25 Static Gestures (SGs) and up to 99.0% for 10 Dynamic Gestures (DGs). The results are on par or better than similar studies.