Yes / Industry-standard quality management systems such as Six Sigma and emerging Industry 4.0 compliant production processes demonstrate the importance of in-line condition monitoring of manufacturing methods for achieving the highest levels of product quality. Measurement data collected as the process is running can inform the operator about unexpected changes in machine operation or raw materials that could negatively impact production; and offer an opportunity for a process control intervention to stabilise production. However, micro-manufacturing production lines can pose a challenging environment for deploying such systems, since processing events can occur extremely rapidly and in harsh environments. Moreover, the small scale of micro-nano featured components can make sensor installation even more problematic. Recently, ultrasonic micromoulding has drawn attention in niche markets due to its unique advantages for processing thermoplastics as a new micro-manufacturing technology. The process differs from conventional moulding significantly by eliminating the need for a plasticising screw and using direct application of ultrasonic energy to melt the polymer. This offers numerous benefits such as decrease in energy usage, moulding at lower pressures, easier cleaning, and reduced material residence times, the latter which could be beneficial for pharma-grade polymers or polymers with active ingredients. However, very little work has been reported attempting to monitor the process using in-line measurements. This work aims to evaluate the characteristics of the ultrasonic micromoulding process for microinjection moulding of a microneedle array using a range of sensor technologies including: data recorded by the machine controller; a high-speed thermal camera and a cavity pressure transducer. The data has captured the highly dynamic process environment with a high degree of accuracy. The relationship between the process data and dimensional quality of the ultrasonically micromoulded products has been quantified and subsequently implemented as a cost-effective in-line quality assurance method. / Horizon 2020, the EU Framework Programme for Research and Innovation (Project ID: 674801). This research has also received funding and support from two other Horizon 2020 projects: HIMALAIA (Grant agreement No. 766871) and Laser4Fun (GA no. 675063)