The ability to accurately predict exhaust system acoustics, including transmission loss (TL) and tailpipe noise, based on CAD geometry has long been a requirement of most OEM’s and Tier 1 exhaust suppliers. Correlation to measurement data has been problematic under various operating conditions, including flow. This study was undertaken to develop robust modelling technique, ensuring sensible correlation between the 1-D models and test data. Ford use Ricardo WAVE as one of their 1-D NVH tools, which was chosen for the purpose of this benchmark study. The most commonly used metrics for evaluating the acoustical performance of mufflers are insertion loss (IL), TL, and noise reduction (NR). TL is often the first step of analysis, since it represents the inherent capability of the muffler to attenuate sound if both the source and termination are assumed to be anechoic. It can also be reliably measured and numerically simulated without having to connect to an engine. For the purpose of software validation, TL benchmarking should be the first step. This study focused around two key exhaust components: Catalysts and Diesel Particulate Filter (DPF), with TL measurements collected for validation purposes. These measurements were compared to simulation data to validate Ricardo WAVE models that directly reflected the test set up. Differences were identified, and model sensitivities studies conducted resulting in revised modeling recommendations for CAT’s and DPF’s. This resulted in more accurate modelling of TL before any hardware is procured.