This paper presents a methodology based on deflection basin parameters and artificial neural networks (ANNs) for processing dynamic falling weight deflectometer (FWD) measurements to estimate layer moduli and condition. Two-dimensional, dynamic, finite element analysis using the ABAQUS program was employed to develop the deflection information for this study. Unlike the majority of the existing backcalculation programs that iteratively adjust all the layer moduli to match the measured deflections, the proposed method first determines the subgrade modulus by means of two deflection basin parameters, Base Damage Index and Shape Factor F2, and then applies the estimated subgrade modulus and other parameters as input variables to a trained ANN to estimate the upper layers’ moduli. Procedures in predicting layer moduli for both two- and three-layer pavement systems are presented. Field FWD measurements were analyzed both by this method and by the MODULUS program, the results of which assess the capability of the proposed method. Effects of discontinuities in the asphalt layer on the resulting FWD deflections were also studied using the finite element method. It was discovered that distresses in the asphalt layer may be detected using two deflection basin parameters, Shape Factor F2 and AREA.