We have built a high resolution towed digital imaging system (ISIIS) capable of imaging water volumes sufficient to accurately quantify even rare plankton (e.g. larval fish) in situ. This imaging system produces very high resolution imagery at very high data rates necessitating automated image analysis. As we are interested in the identification and quantification of a large number of organisms, some of which are relatively similar to each other, we are developing an automated system for detection and recognition of organisms of interest using computer vision tools. Our method aims at (i) the detection of multiple regions (organisms) of interest automatically, while filtering out noise and out-of-focus organisms, and (ii) the classification of the detected organisms into pre-defined categories using shape and texture information. For the organisms detection, we use a probabilistic scheme based on image statistics to locate the regions of interest and - based on size and shape constraints - we filter out the noise, i.e., regions that are detected but do not correspond to organisms. For the classification of the detected organisms, we use the Scale Invariant Feature Transform (SIFT) for matching between the detected regions and the organism images in our database ("dictionary").