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This paper presents a novel method for online tool recognition in manual assembly processes. The goal was to develop and implement a method that can be integrated with existing Human Action Recognition (HAR) methods in collaborative tasks. We examined the state-of-the-art for progress detection in manual assembly via HAR-based methods, as well as visual tool-recognition approaches. A novel online tool-recognition pipeline for handheld tools is introduced, utilizing a two-stage approach. First, a Region Of Interest (ROI) was extracted by determining the wrist position using skeletal data. Afterward, this ROI was cropped, and the tool located within this ROI was classified. This pipeline enabled several algorithms for object recognition and demonstrated the generalizability of our approach. An extensive training dataset for tool-recognition purposes is presented, which was evaluated with two image-classification approaches. An offline pipeline evaluation was performed with twelve tool classes. Additionally, various online tests were conducted covering different aspects of this vision application, such as two assembly scenarios, unknown instances of known classes, as well as challenging backgrounds. The introduced pipeline was competitive with other approaches regarding prediction accuracy, robustness, diversity, extendability/flexibility, and online capability.