Volcanic ash fall-out represents a serious hazard for air and road traffic. The forecasting models used to predict its time-space evolution require information about characteristic parameters such as the ash granulometry. Typically, such information is gained by spot direct observation of the ash at the ground or by using expensive instrumentation. In this paper, a vision-based methodology aimed at the estimation of the ash granulometry is presented. A dedicated image processing paradigm has been developed and implemented in LabVIEW™. The methodology has been validated experi-mentally using digital images and the accuracy of the image processing paradigm has been estimated.