In this paper, we consider advanced image processing and artificial intelligence based techniques for design of fiber optic statistical mode sensors. Output from an optical fiber exhibits a speckle pattern when projected on a flat surface, and intensity, size, and location of these speckles do change with external effects like pressure, temperature, vibration, etc. Statistical mode sensors reported in the literature use global image differencing or global correlation like approaches for sensor design. Namely, lots of localized information in the image is not taken into account, instead global difference and/or correlation are used for sensor construction. We propose the use of localized information with image processing techniques; generate more features, and analysis of these via artificial intelligence based methods. In this way, we can capture more information from the speckle pattern distribution, and hence design a better sensor.© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.