This paper evaluates the capacity of an automated algorithm to detect bubbles and estimate bubble size (Sauter mean diameter, D₃₂) from images recorded in industrial flotation machines. The algorithm is previously calibrated from laboratory images. The D₃₂ results are compared with semi-automated estimations, which are used as "ground truth". Although the automated algorithm is reliable to estimate bubble size at laboratory scale, a significant bias is observed from industrial images for D₃₂ > 3.0-4.0 mm. This uncertainty is caused by the presence of small and large bubbles in the same population, with large bubbles forming complex clusters and being observed incomplete, limited by the region of interest. Flotation columns are more prone to this condition, which hinders the estimation of Sauter diameters. The results show the need for bubble size databases that include industrial images. As several image processing tools are currently available, software calibration from ideal bubble images (synthetic or from laboratory rigs) will mostly lead to biased D₃₂ estimations in industrial flotation machines.