Condition Monitoring (CM) of dynamic vehicle track interaction is an important research topic in rail vehicle dynamics. The most cost-effective method for CM is through carbody floor mounted accelerometers because this is most safe and reliable location for onboard accelerometers onboard inservice train. However, the dynamic response of carbody is influenced not only by excitations coming from track but also by various nonlinearities such as wheel-rail interface and vehicle suspension elements. Thus, it is very challenging to accurately monitor track subsystems via carbody floor accelerations. In this article, two feature extraction algorithms are proposed with the objective of obtaining crucial information on the stability of vehicle using carbody floor accelerations. The first algorithm is based on spectral analysis and the latter is on adaptive signal processing technique. The first algorithm calculates transfer function between track irregularities and carbody floor acceleration using Multiple Input Multiple Output (MIMO) system identification method. The later method analyses the carbody floor accelerations with Empirical Mode Decomposition followed by Singular Value Decomposition (EMD+SVD). These algorithms are evaluated on simulated carbody floor accelerations obtained with vehicle dynamic simulations. In this investigation, it is observed that the first method extracts more crucial information from carbody floor acceleration in comparison to EMD+SVD method. These features are planned to be used in future research to develop machine learning based intelligent fault identification algorithm for identification of root cause of vehicle running instability occurrence.