A recent Illinois Center for Transportation (ICT) research project at the University of Illinois has aimed at evaluating the use of falling weight deflectometer in the assessment of structural conditions of in-service low volume roads that are in need of rehabilitation. Ten different pavement sections were selected from four counties in Illinois with varying structural and traffic characteristics to conduct falling weight deflectometer (FWD) tests. A neural-network based pavement analyzer, ANN-Pro, previously developed at the University of Illinois at Urbana-Champaign based on ILLI-PAVE finite element program solutions, was used to analyze the FWD data in order to determine and monitor the structural adequacies of existing pavement sections. This paper presents a new mechanistic-empirical (M-E) overlay design method that was introduced as part of the ICT research project to adequately assess the structural conditions of existing pavements and subsequently recommend required overlay thickness values from critical pavement responses computed from FWD field deflections. The M-E overlay design methodology compares the critical pavement responses to threshold values for the pre-established fatigue and/or rutting damage algorithms.