The growing trend of model-based design in off-road vehicle engineering requires models that are sufficiently accurate for their intended application if they are to be used with confidence. Uncertain model parameters are often identified from measured data collected in experiments by using an optimization procedure, but it is important to understand the limitations of such a procedure and to have methods available for assessing the uniqueness and confidence of the results. The concept of model identifiability is used to determine whether system measurements contain enough information to estimate the model parameters. A numerical approach based on the profile likelihood of parameters was utilized to evaluate the local structural and practical identifiability of a tractor and single axle towed implement model with six uncertain tire force model parameters from tractor yaw rate and implement yaw rate data. The analysis first considered datasets generated from simulation of the model with known parameter values to examine the effect of measurement error, sampling rate, and input signal type on the identifiability. The results showed that the accuracy and confidence of identification tended to decrease as the quality, quantity, and richness of the data decreased, to the point that some of the parameters were considered practically unidentifiable from the information available. The profile likelihood plots also indicated potential opportunities for model reduction. Second, the analysis considered the identifiability of the model from two datasets collected during field experiments, and the results again indicated parameters that were practically unidentifiable from the information available. Overall, the study showed how different experimental factors can affect the amount of information available in a dataset for identification and that error in the measured data can propagate to error in model parameter estimates.