When checking the ice shape calculation software， its accuracy is judged based on the proximity between the calculated ice shape and the typical test ice shape. Therefore， determining the typical test ice shape becomes the key task of the icing wind tunnel tests. In the icing wind tunnel test of the tail wing model of a large amphibious aircraft， in order to obtain accurate typical test ice shape， the Romer Absolute Scanner is used to obtain the 3D point cloud data of the ice shape on the tail wing model. Then， the batch-learning self-organizing map （BLSOM） neural network is used to obtain the 2D average ice shape along the model direction based on the 3D point cloud data of the ice shape， while its tolerance band is calculated using the probabilistic statistical method. The results show that the combination of 2D average ice shape and its tolerance band can represent the 3D characteristics of the test ice shape effectively， which can be used as the typical test ice shape for comparative analysis with the calculated ice shape.