In this paper, we propose a novel Sign Language Recognition (SLR) model that leverages the task-specific knowledge to incorporate Top-Down (TD) attention to focus the processing of the network on the most relevant parts of the input video sequence. For SLR, this includes information about the hands' shape, orientation and positions, and motion trajectory. Our model consists of three streams that process RGB, optical flow and TD attention data. For the TD attention, we generate pixel-precise attention maps focusing on both hands, thereby retaining valuable hand information, while eliminating distracting background information. Our proposed method outperforms state-of-the-art on a challenging large-scale dataset by over 2%, and achieves strong results with a much simpler architecture compared to other systems on the newly released AUTSL dataset [1].