We evaluated the concept of surface-consistent matching filters for processing time-lapse seismic data, in which matching filters are convolutional filters that minimize the sum-squared error between two signals. Because in the Fourier domain a matching filter is the spectral ratio of the two signals, we extended the well-known surface-consistent hypothesis such that the data term is a trace-by-trace spectral ratio of two data sets instead of only one (i.e., surface-consistent deconvolution). To avoid unstable division of spectra, we computed the spectral ratios in the time domain by first designing trace-sequential, least-squares matching filters, then Fourier transforming them. A subsequent least-squares solution then factored the trace-sequential matching filters into four operators: two surface-consistent (source and receiver) and two subsurface-consistent (offset and midpoint). We evaluated a time-lapse synthetic data set with nonrepeatable acquisition parameters, complex near-surface geology, and a variable subsurface reservoir layer. We computed the four-operator surface-consistent matching filters from two surveys, baseline and monitor, then applied these matching filters to the monitor survey to match it to the baseline survey over a temporal window where changes were not expected. This algorithm significantly reduced the effect of most of the nonrepeatable parameters, such as differences in source strength, receiver coupling, wavelet bandwidth and phase, and static shifts. We computed the normalized root mean square difference on raw stacked data (baseline and monitor) and obtained a mean value of 70%. This value was significantly reduced after applying the 4C surface-consistent matching filters to about 13.6% computed from final stacks.