Comprehensive two-dimensional LC (LC x LC) is a powerful tool for analysis of complex biological samples. With its multidimensional separation power and increased peak capacity, LC x LC generates information-rich, but complex, chromatograms, which require advanced data analysis to produce useful information. An important analytical challenge is to classify samples on the basis of chromatographic features, e.g., to extract and utilize biomarkers indicative of health conditions, such as disease or response to therapy. This study presents a new approach to extract comprehensive non-target chromatographic features from a set of LC x LC chromatograms for sample classification. Experimental results with urine samples indicate that the chromatographic features generated by this approach can be used to effectively classify samples. Based on the extracted features, a support vector machine successfully classified urine samples by individual, before/after procedure, and concentration with leave-one-out and replicate K-fold cross-validation. The new method for comprehensive chromatographic feature analysis of LC x LC separations provides a potentially powerful tool for classifying complex biological samples.