Placing objects on flat surfaces is a crucial skill to master for robots in household environments.Common object-placing approaches require either complete scene specifications or (extrinsic) vision systems, which occasionally suffer from occlusions. Rather than relying on indirect measurements, we propose a novel approach for stable object placing that leverages tactile feedback from an object grasp. We devise a neural architecture called PlaceNet that estimates a rotation matrix, resulting in a corrective gripper movement that aligns the object with the placing surface for the subsequent object manipulation.Our evaluation compares different sensing modalities to each other and PlaceNet to classical, non-learning approaches to assess whether a data-driven approach is indeed required.Applying PlaceNet to a set of unseen everyday objects reveals significant generalization of our proposed pipeline, suggesting that tactile sensing plays a vital role in the intrinsic understanding of robotic dexterous object manipulation.Code, models, and supplementary videos will be made available upon acceptance.