Event-related brain potentials (ERPs) are important research tools because they provide insights into mental processing at high temporal resolution. Their usefulness, however, is limited by the need to average over a large number of trials, sacrificing information about the trial-by-trial variability of latencies or amplitudes of specific ERP components. Here we propose a novel method based on an iteration strategy of the residues of averaged ERPs (RIDE) to separate latency-variable component clusters. The separated component clusters can then serve as templates to estimate latencies in single trials with high precision. By applying RIDE to data from a face-priming experiment, we separate priming effects and show that they are robust against latency shifts and within-condition variability. RIDE is useful for a variety of data sets that show different degrees of variability and temporal overlap between ERP components.