In this talk we will quickly review the general approaches followed by the research community to solve the Sign Language Recognition (SLR) problem in the pre-deep learning era and then review, also briefly, the latest architectures using DNNs. These data-hungry models pose a very important problem in this specific task due to the scarcity of labeled data. In the last 5 years there has been a great deal of effort on compiling labeled datasets of Word-Level SLR and Continuous-SLR, but we are still very far from the amount of data readily available for other speech-based tasks. Acquiring SLR has the double challenge of needing donors that are scarce and needing SLR interpreters that help with the logistics, curation and labeling of the dataset. The GTM group at the atlanTTic Center in the University of Vigo has started this research line three years ago. We will show the state of the project nowadays and the state of the dataset we are acquiring with the help of Galician deaf associations and SL interpreters. We will also show the different approaches we are following both for understanding manual and facial components of the sign language and the latest results on Word-Level SLR.