This work advances a neural network that characterizes aircraft ice accretion in order to improve flight performance and safety. Neural networks have been developed previously for use within an ice management system that monitors inflight aircraft icing and its effects upon performance, stability, and control. The previous work has applied these networks to stability and control derivative estimates provided by an H°° parameter identification algorithm during a longitudinal maneuver. This paper extends those results by addressing ice characterization in the absence of pilot input when poor excitation of the flight dynamics limits the accuracy of parameter estimates. To compensate for this shortcoming inherent to steady-level flight scenarios, the neural network presented in this paper integrates steady-state characterization and hinge moment sensing with parameter estimates. The neural network provides icing characterization in terms of an estimate of the previously developed icing severity factor, rj. Extensive simulation results are presented that indicate the accuracy of neural network characterization during steady-level flight in the presence of sensor noise and turbulence over a broad range of flight trim conditions and turbulence levels. Furthermore, the relative utility of each information source is investigated via consideration of network accuracy of networks trained only on that information source.