This paper is concerned with the methodologies in statistical image processing and recognition. Specific areas considered are the following: (1) The decision rules in image recognition and their comparative evaluation under finite sample size condition; (2) Statistical feature extraction techniques for image segmentation with emphasis on the statistical characteristic of textural features; (3) Statistical contextual analysis algorithms for images. Emphasis is placed on the contextual pre processing/postprocessing techniques to implement the optimum decision rules with context; (4) Statistical image modelling techniques including the nonhomogeneous models and the autoregressive models. The software problems involved in these areas are also examined in details.