Supervised machine learning techniques have been used in medical imaging and aim to reduce subjectivity and improve quantitative results. When handling heterogeneous MR datasets, most algorithms require pre-processing, such as intensity normalization. Here, the influence of MR normalization techniques on supervised classification is evaluated using handcrafted textural and convolutional features. These features combined can differentiate control subjects from atherosclerosis patients using only imaging scans. Non-significant statistical difference in classification was found across intensity normalization methods, demonstrating little influence of this pre-processing step on the supervised classification outcome.