In large volume manufacturing, moving heavy components around manufacturing facilities to machine features on them can represent a significant proportion of the parts final cost because the tools used for this require a high initial investment and operating costs. This motivates interest in robotic machining so that the “process-to-part” concept can be employed. However, typical industrial robots lack the positional capability of conventional equipment, which ultimately results in dimensional errors in the features machined. This research investigates accumulation of error originating from non-cutting stages of robotic machining programs, using a hexapod robot. This is done using a procedure adapted from ISO 9283–Manipulating Industrial Robots—Performance Criteria and Related Test Methods to determine positional accuracy and repeatability, i.e. systematic and random errors. This concludes that, although the robot encounters high levels of error prior to cutting, a portion of these may be offset with in-situ condition monitoring to facilitate higher tolerance machining. Potential is therefore found for using robot machining for manufacturing cost reduction in the large-scale manufacturing industries.