This paper presents symbolic pattern analysis of sidescan sonar images for detection of mines and mine-like objects in the underwater environment. For robust feature extraction, sonar images are symbolized by partitioning the data sets based on the information generated from the ground truth. A binary classifier is constructed for identification of detected objects into mine-like and non-mine-like categories. The pattern analysis algorithm has been tested on sonar data sets in the form of images, which were provided by the Naval Surface Warfare Center. The algorithm is designed for real-time execution on limited-memory commercial-of-the-shelf platforms, and is capable of detecting seabed-bottom objects and vehicle-induced image artifacts.