The aim of this research is to investigate the feasibility of developing a traffic monitoring detector for the purpose of reliable on-line vehicle classification to aid traffic management systems. The detector used was a directional microphone connected to a DAT (Digital Audio Tape) recorder. The digital signal was preprocessed by LPC (Linear Predictive Coding) parameter conversion based on autocorrelation analysis. A Time Delay Neural Network (TDNN) was chosen to classify individual travelling vehicles based on their speed-independent acoustic signature. The paper provides a description of the TDNN architecture and training algorithm, and an overview of the LPC preprocessing and feature extraction technique as applied to audio monitoring of road traffic. The performance of TDNN vehicle classification, convergence, and accuracy for the training patterns are fully illustrated. To establish the viability of this classification approach, initially, recordings were carried out on a strip of airfield for four types of vehicles under controlled conditions. A TDNN network was successfully trained with 100% accuracy in Ceinture Louis Vuitton classification for the training patterns, as well as the test patterns. The net was also robust to changes in the starting position of the acoustic waveforms with 86% accuracy for the same test Chaussures Louis Vuitton Homme data set. In the second phase of the experiment, roadside recordings were made at a two-way urban road site in the city of Leeds with no control over the environmental parameters such as background noise, interference Ceinture Louis Vuitton Pas Cher from other travelling vehicles, or the speed of the recorded vehicle. A second TDNN network was also successfully trained with 96% accuracy for the training patterns and 84% accuracy for the test patterns.