The principal intent of this research is to: (a) investigate the potential of passive microwave data from AMSU in detecting snowfall events and in measuring their intensity, and (b) evaluate the effect of both land cover and atmospheric conditions on the retrieval accuracy. A neural-network-based model has been developed and has shown a great potential in detecting and estimating the intensity of snowfall events. This algorithm has been applied for different snow storms occurred in four winter seasons in the North-East of United States. Additional information such as cloud cover and air temperature were added to the process to reduce misidentified snowfall pixels. Only pixels with cloud cover and falling within a specific range of temperature are presented to the snowfall detection model. Surface temperature collected from ground station-based observations and archived by the National Climatic Data Center (NCDC) were used for this test. Different heavy storm events and non-snowfall observations that occurred at the same time as AMSU acquisition were selected. Hourly snow accumulation data collected by the NCDC were used as truth data to train and validate the model. The results indicate that the neural-network-based model provides a significant improvement in snowfall detection accuracy over existing satellite-based methods. Most importantly, the neural network system product is a map indicating the snowfall area and the respective intensity level for each pixel.