Shadow removal is an important topic in image restoration, and it can benefit many computer vision tasks. State-of-the-art shadow-removal methods typically employ deep learning by minimizing a pixel-level difference between the de-shadowed region and their corresponding (pseudo) shadow-free version. After shadow removal, the shadow and non-shadow regions may exhibit inconsistent appearance, leading to a visually disharmonious image. To address this problem, we propose a style-guided shadow removal network (SG-ShadowNet) for better image-style consistency after shadow removal. In SG-ShadowNet, we first learn the style representation of the non-shadow region via a simple region style estimator. Then we propose a novel effective normalization strategy with the region-level style to adjust the coarsely re-covered shadow region to be more harmonized with the rest of the image. Extensive experiments show that our proposed SG-ShadowNet outperforms all the existing competitive models and achieves a new state-of-the-art performance on ISTD+, SRD, and Video Shadow Removal benchmark datasets. Code is available at: https://github.com/jinwan1994/SG-ShadowNet.