Automatisation and standardisation of the diagnosis process in medical infrared thermal imaging (MITI) is crucial because the number of medical experts in this area is highly limited.The current studies generally need manual intervention. One of the manual operations requires physician's determination of the body part and orientation. In this study automatic pose and body part detection on medical thermal images is investigated. The database (957 thermal images - 59 patients) was divided into four classes upper-lower body parts with back-front views. First, histogram equalization (HE) method was applied on the pixels only within the body determined using Otsu'sthresholding approach. Secondly, DarkNet-19 architecture was used for feature extraction, and principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE) approaches for feature selection. Finally, the performances of various machine learning based classification methods were examined. Upper vs. lower body parts and back vs. front of upper body were classified with 100% accuracy, and back vs. front classification of lower body part success rate was 93.38%. This approach will improve the automatisation process of thermal images to group them for comparing one image with the others and to perform queries on the labeled images in a more user-friendly manner.