The increasing demand for product customisation is leading to higher complexities within manufacturing. This imposes new challenges for the workforce. One way to support operators’ productivity may be context-aware, human-centred cyber-physical assistance systems. Human Activity Recognition (HAR) is a promising approach to enable context-awareness. However, standardised approaches to integrate HAR into existing manufacturing environments are rare. Particularly, there is a lack of available datasets of manufacturing activities. Moreover, comparative studies of inertial and visual HAR approaches are still rare. This work therefore proposes Methods-Time Measurement (MTM) as a standardised foundation for creating a manufacturing activity dataset. Subsequently, five different machine learning algorithms are tested for their recognition performance based on the dataset captured with an inertial sensor suit and an RGB-D sensor. A proof-of-concept is delivered for both sensor categories applied to the scope of 18 MTM-1 activities, whereas inertial data outperformed depth data. K-Nearest Neighbour and Bagged Tree algorithms revealed the best classification accuracy results in this context.