Two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC \(\times \) GC ToF-MS) provides detailed chemical profiles of complex mixtures, making it useful in areas such as environmental monitoring and medical diagnostics. A promising application is sex classification from human scent, where subtle chemical differences indicate biological sex. In this paper, we propose a pattern recognition approach to sex classification that interprets raw GC \(\times \) GC ToF-MS data as images, moving beyond traditional compound-based analysis. Our approach employs convolutional neural networks (CNNs) to analyze these images, and we compare its performance against established techniques – linear SVM, Ridge regression, and QDA – demonstrating robust and competitive results. Furthermore, we introduce and release a new dataset of GC \(\times \) GC ToF-MS measurements to support reproducibility in future studies. Using an identity-aware cross-validation strategy, where test subjects are completely unseen during training, our method achieves approximately 88% accuracy on 504 measurements from 40 individuals.