Oil and natural gas are normally transported through a vast network of pipelines, a major segment of which are seamless pipes. The manufacturing processes associated with the production of seamless pipes introduces an artifact known as seamless pipe noise (SPN), in the data obtained from magnetic flux leakage (MFL) inspection of these pipelines. SPN poses a major challenge in the flaw detection and characterization as it can overwhelm the flaw response and can therefore, mask flaw signature in MFL data. In this paper, we present a texture analysis approach to automatically detect flaws while characterizing the textures of SPN and flaw response using gray level co-occurrence matrix (GLCM). The motivation to propose texture analysis approach is to investigate the statistical properties of these signals. The proposed method provides a recognition rate of 97.29% for flaws which are deeper than 20% of the wall thickness of the pipe, thus confirming the existence of different textural characteristics for SPN and flaw response.