Registration of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) of soft tissue is difficult. Conventional registration cost functions that depend on information content are compromised by the changing intensity profile, leading to misregistration. We present a new data-driven model of uptake patterns formed from a principal components analysis (PCA) of time-series data, avoiding the need for a physiological model. We term this process progressive principal component registration (PPCR). Registration is performed repeatedly to an artificial time series of target images generated using the principal components of the current best-registered time-series data. The aim is to produce a dataset that has had random motion artefacts removed but long-term contrast enhancement implicitly preserved. The procedure is tested on 22 DCE-MRI datasets of the liver. Preliminary assessment of the images is by expert observer comparison with registration to the first image in the sequence. The PPCR is preferred in all cases where a preference exists. The method requires neither segmentation nor a pharmacokinetic uptake model and can allow successful registration in the presence of contrast enhancement.