Low adhesion in the wheel/rail contact or the 'leaves on the line' problem is a large operational issue for the railway industry. There is currently a shortage of up to date information about the running conditions of rails with respect to short term adhesion trends (over a daily period) and macro trends (across seasons). This can lead to costly over application of mitigation actions such as rail head cleaning to combat the problem. The generally established methods of assessing areas of low adhesion involve mapping activations of wheel slide and wheel slip protection events to track locations. These methods are reactive and rely upon a slip/slide event to be initiated by the application of traction or braking. The RSSB managed project T959 is forwarding previous fundamental research into methods of low adhesion detection (LAD) in real time using 'modest cost' inertial sensors mounted to in service vehicles. The LAD system proposes that the motions of a railway vehicle (in both lateral and yaw movements) vary as the adhesion conditions under the vehicle change. If the changes in the running dynamics as a result of low adhesion can be observed and interpreted, they can infer the adhesion at all points across a network and not just when slip/slide events are triggered. The main focus of the research has been to use a linearised model of a rail vehicle suspension system to define a Kalman-Bucy filter. The creep forces, that are unable to be measured directly, are outputted from this filter as augmented states. By comparing the estimated values of creep forces against measurable vehicle dynamics, an estimation of adhesion level can be realised. This approach has been verified against rail vehicle simulations performed by DeltaRail using the multi-body physics software VAMPIRE®.