Objective quality assessment for 3D printing purposes may be considered as one of the most useful applications of machine vision in smart monitoring related to the development of the Industry 4.0 solutions. During recent years several approaches have been proposed, assuming observing the side surfaces, mainly based on the analysis of the regularity of visible patterns, which represent the consecutive printed layers. These methods, based on the use of general purpose image quality assessment (IQA) metrics, Hough transform, entropy and texture analysis, make it possible to classify the printed samples, independently of the filament’s colour, into low and high quality classes, with the use of photos or 3D scans of the side surfaces. The next step of research, investigated in this paper, is the combination of various proposed approaches to develop a combined metric, possibly highly correlated with subjective opinions. Since the correlation of single metrics developed mainly for classification is relatively low, their combination makes it possible to achieve much better results, verified using an original, newly developed database containing 107 captured images and 3D scans of the 3D printed surfaces with various colours and local distortions caused by external factors, together with Mean Opinion Scores (MOS) gathered from independent observers. Obtained results are promising and may be a starting point for further research towards the optimisation of the newly developed metrics for the automatic assessment of the 3D printed surfaces, mainly for aesthetic purposes.