Snow accumulations, especially if of great intensity, as is the case in northern countries, for example, can be very damaging, especially if they occur in urban environments. The damage provoked by snow is not only related to the weight of the accumulations, causing damage to structures but also to the pollution retained by the structure of the snowflakes. However, snow weight monitoring is a complex task, both because of the placement of the sensors and the specific operating ranges they must have in terms of operating temperature. These complications can be overcome by the design and use of a soft sensor, that is, a sensor capable of making indirect measurements from other parameters related to the measurement under consideration. This paper presents the design and metrological validation of a soft sensor for indirect weight measurement of snow accumulations. The designed soft sensor has been based on Artificial Neural Network and achieved, as a result, a Root-Mean-Square Error (RMSE) of 114g and a maximum extended uncertainty, evaluated by Monte Carlo simulation, of 300g in a measurement range from 150g to 5200g.