We have developed an adaptive, automatic, correlation- and clustering-based method for greatly reducing the degree of picking inconsistency in large, digital seismic catalogs and for quantifying similarity within, and discriminating among, clusters of disparate waveform families. Innovations in the technique include (1) the use of eigenspectral methods for cross-spectral phase estimation and for providing subsample pick lag error estimates in units of time, as opposed to dimensionless relative scaling of uncertainties; (2) adaptive, cross-coherency-based filtering; and (3) a hierarchical waveform stack correlation method for adjusting mean intercluster pick times without compromising tight intracluster relative pick estimates. To solve the systems of cross-correlation lags we apply an iterative, optimized conjugate gradient technique that minimizes an L1-norm misfit. Our repicking technique not only provides robust similarity classification-event discrimination without making a priori assumptions regarding waveform similarity as a function of preliminary hypocenter estimates, but also facilitates high-resolution relocation of seismic sources. Although knowledgeable user input is needed initially to establish run-time parameters, significant improvement in pick consistency and waveform-based event classification may be obtained by then allowing the programs to operate automatically on the data. The process shows promise for enhancing catalog reliability while at the same time reducing analyst workload, although careful assessment of the automatic results is still important.