Data processing and identification of unknown compounds in comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC×GC/TOFMS) analysis is a major challenge, particularly when large sample sets are analyzed. Herein, we present a method for efficient treatment of large data sets produced by GC×GC/TOFMS implemented as a freely available open source software package, Guineu. To handle large data sets and to efficiently utilize all the features available in the vendor software (baseline correction, mass spectral deconvolution, peak picking, integration, library search, and signal-to-noise filtering), data preprocessed by instrument software are used as a starting point for further processing. Our software affords alignment of the data, normalization, data filtering, and utilization of retention indexes in the verification of identification as well as a novel tool for automated group-type identification of the compounds. Herein, different features of the software are studied in detail and the performance of the system is verified by the analysis of a large set of standard samples as well as of a large set of authentic biological samples, including the control samples. The quantitative features of our GC×GC/TOFMS methodology are also studied to further demonstrate the method performance and the experimental results confirm the reliability of the developed procedure. The methodology has already been successfully used for the analysis of several thousand samples in the field of metabolomics.