A neural network approach was used to develop acccurate algorithms for inverting a complex forest backscatter model. The model combines a forest growth model with a radar backscatter model. The forest growth model captures natural variations of forest stands (e.g., growth, regeneration, death, multiple species and competition for light). This model was used to produce vegetation structure data typical of transitional/northern boreal hardwood forests in Maine. These data supplied inputs to the radar backscatter model which simulated the polarimetric radar backscatter (C, L, P, X bands) above the forests. Using these simulated data, various neural networks were trained with inputs of different backscatter bands and output parameters of above ground biomass, total number of trees, mean tree height and mean tree age. These trained neural networks act as efficient algorithms for inverting the complex forest backscatter model. The accuracies (r.m.s. and R2 values) for inferring various parameters from radar backscatter were above ground biomass (1.6kg m -2, 0.94), number of trees (48 ha -1, 0.94), tree height (0.47 m, 0.88) and tree age (24.0 years, 0.83). The networks that used only AIRSAR bands (C, L, P) had a high degree of accuracy. The inclusion of the X band with the AIRSAR bands did not seem to increase significantly the accuracy of the networks. The networks that used only the C and L bands still had a relatively high degree of accuracy for all forest parameter (R2 values from 0.75 to 0.91). Modest accuracies (R2 values from 0.65 to 0.84) were obtained with networks that used only the L band and poor accuracies (R2 values from 0.36 to 0.46) were obtained with networks that used only the C band. Several networks were shown to be relatively insensitive to the addition of random noise to radar backscatter. The results demonstrate that complex, forest backscatter models can be efficiently inverted using neural networks that use only radar backscatter data.