We present an algorithm and program called Pattern Picker that performs editing of raw peak lists derived from multidimensional NMR experiments with characteristic peak patterns. Pattern Picker detects groups of correlated peaks within peak lists from reduced dimensionality triple resonance (RD-TR) NMR spectra, with high fidelity and high yield. With typical quality RD-TR NMR data sets, Pattern Picker performs almost as well as human analysis, and is very robust in discriminating real peak sets from noise and other artifacts in unedited peak lists. The program uses a depth-first search algorithm with short-circuiting to efficiently explore a search tree representing every possible combination of peaks forming a group. The Pattern Picker program is particularly valuable for creating an automated peak picking/editing process. The Pattern Picker algorithm can be applied to a broad range of experiments with distinct peak patterns including RD, G-matrix Fourier transformation (GFT) NMR spectra, and experiments to measure scalar and residual dipolar coupling, thus promoting the use of experiments that are typically harder for a human to analyze. Since the complexity of peak patterns becomes a benefit rather than a drawback, Pattern Picker opens new opportunities in NMR experiment design.