Craniomandibular bone structures, as they are more resistant to taphonomy processes, are relevant in the sexual diagnosis of adult skeletons. This step is essential in the reconstruction of an unidentified corpse. Within this context, this study evaluates the performance of sexual classification methodologies based on 206 orthopantomographs. Hence, convolutional neural networks (CNN) were applied directly on the orthopantomography, and several classification methodologies were applied to linear measurements taken on the orthopantomographs, such as logistic regression, discriminant analysis, k-nearest neighbours, naïve Bayes, support vector machines, decision trees, and random forests. The performance of each method was evaluated based on accuracy, sensitivity, specificity, predictive values, and the area under the ROC curve. The pre-trained VGG16 CNN achieved better results, revealing that it can be reliably applied in sexual classification in a Portuguese adult population within the scope of forensic science. Nonetheless, a final sexual classification model to be applied to the Portuguese population must be established in a larger sample.