Airborne cloud observation relies on high-frequency black-and-white-image information. The study presents automatic shape recognition tools developed with machine learning techniques and adapted for this type of image. Applied on a recent field campaign, these tools produce morphology-specific size distributions that can be compared across four instruments covering different size ranges. The analysis show that the tools are performing well and are consistent across the different instruments.