We present a generic and robust sim-to-real deep-learning-based framework, namely S2R-Pick, for fast and accurate object recognition and localization in industrial robotic bin picking. Unlike existing works designed for general everyday environments, objects for industrial bin picking are often texture-less, metallic, and also suffer from severe occlusion and cluttering. In this work, we first develop an automated synthetic data generation pipeline to produce large-scale photo-realistic data with precise annotations. We then formulate an instance segmentation network for object recognition and a 6D pose estimation network for localization. We cascade and train the two networks only on our generated synthetic data and can directly transfer them for processing real inputs without needing to retrain them on real samples. Extensive experiments show the effectiveness and superiority of our framework over state-of-the-art methods.