In the following work we discuss the application of image processing and pattern recognition to the field of quantitative phycology. We overview the field of image processing, and review previously published literature pertaining to the image analysis of phycological images, and in particular, cyanobacterial image processing. We then discuss the main operations used to process images and quantify data contained within them. To demonstrate the utility of image processing to cyanobacteria classification, we present details of an image analysis system for automatically detecting and classifying several cyanobacterial taxa of Lake Biwa, Japan. Specifically, we initially target the genus Microcystis, for detection and classification from among several species of Anabaena. We subsequently extend the system to classify a total of 6 cyanobacteria species. High-resolution microscope images containing a mix of the above species and other non-targeted objects are analysed, and any detected objects are removed from the image for further analysis. Following image enhancement, we measure object properties and compare them to a previously compiled database of species characteristics. Classification of an object as belonging to a particular class membership (e.g. 'Microcystis', A. smithii, 'Other', etc.) is performed using parametric statistical methods. Leave-one-out classification results suggest a system error rate of approximately 3%.