This paper introduces a complete work-flow for the translation of dynamic isolated signs based on data acquired from a data-glove. A sign language translation system based on a wearable device represents indeed a more efficient solution with respect to cameras or position trackers for helping speech-impaired people on a daily basis. The paper describes the different steps required for a sign language translation, namely segmentation, feature extraction and classification, together with the custom data-glove used for data-acquisition. The paper presents also experimental results, comparing different machine learning classifiers and discussing their performances both in terms of translation accuracy and computational time. The proposed work-flow has been tested both on data acquired from a custom data-glove and on a public database, and it outperforms those of other works in literature. The reported analysis suggests a multi-layer perceptron neural network as the most suitable classifier for the realization of a wearable sign language translation system.