Abstract: This paper issues an aircraft icing classification problem by directly using the flight state variables and probabilistic neural networks (PNN) for meeting the needs of classifying and alarming icing situation during aircraft flight. A dynamic model for inflight icing is presented based on the icing experiment research data of the NASA Twin Otter airplane. The dynamic model simulates five situations, i.e., clean, moderate/severe wing icing, moderate/severe tailplane icing, to generate abundant flight data for the networks’ training and assessment. The influence of icing on different flight state variables is quantitatively analyzed, and the six variables significantly affected by icing are chosen for building the neural networks. A scheme for optimizing the network propagation parameter is presented to improve the classification accuracy, then the performance of the six state variables-based classification networks is analyzed and compared. The results indicate that PNNs built by the six state variables have relatively high accuracy for both the training and validation data. For the training data, the classification accuracy all reaches 100%, while for the validation data, the highest accuracy among all six nets is 99.1%, and the lowest accuracy is still above 85%. Fast variables have a higher accuracy for the validation data than slow variables, but a lower accuracy for the generalization assessment data, whereas the slow variables conduct oppositely. The networks built by the fast variable, angle of attack α and the slow variable, displacement x_e perform relatively better than the others. Without the wind disturbance, the most considerable false alarming probability of α-based networks is below 1.4%. With the wind disturbance, the x_e-based networks are more preferable, as the overall false alarming probability for all the five icing situations is below 10%. Finally, the performance of the optimized PNN is further validated by comparing the classification accuracy with that of two support vector machine methods.