Object tracking plays a vital role in many computer vision systems applications, such as video surveillance, robotics, 3-D image reconstruction, medical imaging, human computer interface, etc. In many proposed approaches, feature-based object tracking is widely used due to its accuracy. Feature extraction and feature correspondence are two main components of feature-based object tracking. In our proposed method, we have used gray level co-occurrence matrix (GLCM) features for object tracking in thermal imagery. As spatial resolution of the thermal sensor is fairly coarse, it also implies that temperature scales are close but may not exactly be the same, which indicates the presence of mutually related pixels or group of pixels. The GLCM texture analysis is based on assumption that the texture information of an image is an average spatial relationship between the gray tones in the image. Thus, this similarity in spatial resolution properties makes GLCM features suitable for object tracking in thermal infrared imagery. Initially, the target blobs to be tracked are provided by object detection stage. Then, for a given target blob in a frame, we first calculate GLCM feature points and then find corresponding features in the next successive frame. The sum of squared differences between two feature point sets is calculated to find feature correspondence between two frames for object tracking. Simultaneously, the codebook of the center of the blobs for prediction of the target candidate region in the next frame is maintained in order to have robust object tracking under occlusions. GLCM-based object tracking in thermal imagery outperforms the color or LBP-based mean-shift approach. This algorithm is also able to track objects in split and merge condition. The accuracy of this algorithm also depends on the object detection stage, such as Kalman tracking.