Automatic sign language translation has gained particular interest in the computer vision and computational linguistics communities in recent years. Given each sign language country’s particularities, machine translation requires local data to develop new techniques and adapt existing ones. This work presents iLSU-T, an open dataset of interpreted Uruguayan Sign Language RGB videos with audio and text transcriptions. This type of multimodal and curated data is paramount for developing novel approaches to understand or generate tools for sign language processing. iLSU-T comprises more than 185 hours of interpreted sign language videos from public TV broadcasting. It covers diverse topics and includes the participation of 18 professional interpreters of sign language. A series of experiments using three state-of-the-art translation algorithms is presented. The aim is to establish a baseline for this dataset and evaluate its usefulness and the proposed pipeline for data processing. The experiments highlight the need for more localized datasets for sign language translation and understanding, which are critical for developing novel tools to improve accessibility and inclusion of all individuals. Our data and code can be accessed at https://github.com/ariel-e-stassi/iLSU-T.