Intelligent models for tool wear condition monitoring (TWCM) have been extensively researched. However, in industrial scenarios, limited acquired monitoring signals and variations of machining parameters lead to insufficient training samples and data distribution shifts for the models. To address the issues, this research presents a novel residual attention hybrid adaptation network (RAHAN) model based on a residual attention network (ResAttNet) and a hybrid adaptation strategy. In the RAHAN model, by integrating a channel attention mechanism and deep residual modules, ResAttNet is designed as a feature extractor to acquire features from monitoring signals for tool wear conditions. Embedding subdomain adaptation into a condition recognizer while establishing separate adversarial learning in a domain obfuscator, the hybrid adaptation strategy is developed to eliminate global distribution shifts and align local distributions of each tool wear phase simultaneously. Six migration tasks under a laboratory and two factory machining platforms were conducted to evaluate the effectiveness of the RAHAN model. Compared with a baseline model, four ablation models, and six state-of-the-art transfer learning models, the RAHAN model achieved the highest average accuracy of 92.70% on six migration tasks. Furthermore, the RAHAN model shows clearer feature representations of each tool wear condition than other compared models. The comparative results demonstrate that the RAHAN model has superior transferability and therefore can be considered as a good potential solution to support cross-domain TWCM under different machining processes.