Studies on monolayer cultures and whole-animal models for the prediction of the response of native human tissue are associated with limitations. Therefore, more and more laboratories are tending towards multicellular spheroids grown in vitro as a model of native tissues. In addition, they are increasingly used in a wide range of biofabrication methodologies. These 3D microspheroids are generated through a self-assembly process that is still poorly characterised, called cellular aggregation. Here, a system is proposed for the automated, non-invasive and high throughput monitoring of the morphological changes during cell aggregation. Microwell patterned inserts were used for spheroid formation while an automated microscope with 4x bright-field objective captured the morphological changes during this process. Subsequently, the acquired time-lapse images were automatically segmented and several morphological features such as minor axis length, major axis length, roundness, area, perimeter and circularity were extracted for each spheroid. The method was quantitatively validated with respect to manual segmentation on four sets of ± 60 spheroids. The average sensitivities and precisions of the proposed segmentation method ranged from 96.67-97.84% and 96.77-97.73%, respectively. In addition, the different morphological features were validated, obtaining average relative errors between 0.78-4.50%. On average, a spheroid was processed 73 times faster than a human operator. As opposed to existing algorithms, our methodology was not only able to automatically monitor compact spheroids but also the aggregation process of individual spheroids, and this in an accurate and high-throughput manner. In total, the aggregation behaviour of more than 700 individual spheroids was monitored over a duration of 16 hours with a time interval of 5 minutes, and this could be increased up to 48,000 for the described culture format. In conclusion, the proposed system has the potential to be used for unravelling the mechanisms involved in spheroid formation and monitoring their formation during large-scale manufacturing protocols.