Autism Spectrum Disorder (ASD) is a developmental disorder that persistently impairs communication and social interaction, with a high degree of clinical heterogeneity. The fact that the diagnosis is based on clinical observation which requires therapy expertise, interviews with parents and application of questionnaires, it contributes to delaying the diagnosis until school age. The study of anthropometric measures in individuals with ASD and individuals in typical development (TD) showed there are some differences between the two groups. However, there is a lack of computational tools to assist in the acquisition and analysis process of these measures. This paper proposes a computer-aided medical decision support system that, given a child’s facial image captured by a digital camera, can discriminate between the two groups, ASD and TD, thus helping in the diagnosis. We defined a protocol for image acquisition and preprocessing, tested and compared methods for dataset balancing, dimension reduction and classification. The best results were obtained by a SVM classifier with 86.2% accuracy. Once the proposed model is based on facial images, it has the potential to facilitate early diagnosis of ASD.