Automatic vehicle type classification plays a significant role in security, traffic control and autonomous driving applications. Thermal infrared (IR) cameras operating even in complete darkness and adverse weather conditions emerge as a potential sensing modality for such challenging outdoor applications. However, automated vehicle type classification in infrared imagery still poses significant challenges due to high variability of vehicle signature in infrared band leading to high intra-class variation and low inter-class variation. To address these issues, we demonstrate the use of local features represented in a bag of words framework. In this work, we present comparative analysis of two feature detectors, MSER – a sparse region based detector and uniform dense sampling of points in the image across multiple scales (termed dense). A bag of features (BoF) framework based on SURF feature descriptor and SVM classifier for vehicle type classification are evaluated on a thermal infrared (TIR) vehicle dataset. A number of variations are present in the TIR vehicle dataset - scale variation, pose variation and partial visibility of vehicles captured under varied environmental conditions. The dataset contains four vehicle categories commonly plying on Indian roads, Bike, Autorickshaw, Car and Heavy vehicle. The performance of the designed vehicle type classification framework was evaluated using performance metrics, classification accuracy and confusion matrix. The optimized sparse MSER and dense BoF framework demonstrated decent classification accuracies of 85.7% and 93% respectively for automatic vehicle type classification on the thermal infrared vehicle dataset.