Fitting LC retention factors to an appropriate model is typically carried out by fitting the retention factors measured under isocratic conditions. However, it can be advantageous to obtain the parameters of the model by fitting gradient data instead. This could potentially expand the range of mobile phase compositions sampled in the experiments and allow for strongly retained solutes to be eluted within reasonable analysis times, as compared to isocratic experiments . We recently reported several difficulties with using the NK model for fitting gradient data . We specifically investigated the ability of the gradient fitting procedure to produce parameters that could accurately estimate retention factors measured under isocratic conditions using simulated data. The results were highly variable, and the best results were obtained using a time-consuming global fitting routine, but even then, the agreement between the retention factors predicted from isocratic data fitting and gradient data fitting was poor in many cases . The two primary limitations that were found were that: 1) the results were highly dependent on the accuracy of the initial guesses used to start the fitting procedure; and 2) there was a lack of uniqueness in the recovered parameters (i.e., multiple sets of parameters produce almost identical fits, especially in the presence of any reasonable level of experimental noise) . We reasoned that the reparameterization of the NK model might alleviate these difficulties, and specifically address this point in the present paper.