A very brief introduction is given to the principles underlying seismic data processing. A model for the generation of synthetic seismograms is presented and discussed. The multichannel Wiener filter is presented and discussed as a data processing tool for the detection of moveout. A number of such filters are computed to pass/reject correlated events of both linear and quadratic moveout. Experiments are performed on a variety of synthetic data, in order to ascertain how such filters will perform in a given situation. It is shown that very low levels of signal can be successfully recovered by multichannel filtering, and that a very close Match must exist between the moveouts present in the data and those specified for the particular filter design. Application of these filters to real data records give results. in good agreement to that expected from a study of the synthetic case. The analytical methods,of (i).the autocorrelation matrix, and (ii) the Fourier Transform, are presented and discussed. The "average" or second order autocorrelation matrix is developed, and a multichannel. filter scan of a column from this matrix is incorporated in the former method. A variety of experiments are performed on syhthetic data, of both linear and quadratic moveout, for different noise conditions in order to ascertain the ability of these methods to detect low power correlated events. It is shown that such detection can be successful, provided that the random noise is not too severe. The benefits of removing the obvious moveouts, by a multichannel filter, from the input data before commencing analysis, is also demonstrated. Results from the analyses of real data records are generally consistent with those of the synthetic case.