Rail vehicle designers and analysts can benefit from the results of vehicle parameter estimation. Using this technique, they can determine the effects of suspension design decisions, and they can reduce the amount of on-track testing required to qualify new designs for service. This work addresses two major issues: the determination of parameter identifiability and the estimation of rail vehicle parameters from laboratory tests. Usually, the identifiability issue should be addressed first since identifiability determines the number of independent parameters that can be estimated. The general issues of identifiability and parameter estimation are discussed. Two identifiability tests are explored in-depth, as is a Bayesian least-squares parameter estimation method. Laboratory tests from a lightweight intermodal rail vehicle with single-axle trucks provided the data for the parameter estimation. The test setup and a simple vehicle mathematical model provided the structure for the identifiability determination. This work shows that identifiability and estimation issues closely interact. Even if a system is not identifiable, the Bayesian estimation method can return results. Thus, the Bayesian method can instill false confidence in the validity of the estimation results. Estimation of experimental data with a linear model provided values within one percent for the mass and damped natural frequency, and ten percent for the peak amplitude. Excellent agreement with the experimental data was obtained for frequencies above the resonant peak and for very low frequencies. Error at frequencies slightly below the resonant peak, however, indicated the vehicle contained significant nonlinearities. To achieve closer agreement between model response and test response at these frequencies, a nonlinear vehicle model is needed. / Master of Science