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Monitoring plays a key role in all aspects of fisheries management, including those related to sustainable management of resources, the economic performance of the fishery, and the distribution of benefits from the exploitation of the fishery and environment. In this manuscript, an electronic device (the iObserver) is described, which aims to improve fisheries monitoring by identifying and quantifying fishing catches on board commercial vessels. This device is located over the conveyor belt in the fishing sorting area to automatically take pictures of the entire catch during fish separation. Each picture is analyzed using open source image recognition software to identify the number of individuals, the species and length of each individual based on skin descriptors (color, texture), and shape. The iObserver is equipped with a graphical and user-friendly interface. The information provided by the iObserver is sent to the RedBox software, where it is aggregated and augmented with vessel instrumentation data, such as location, velocity, and course. Then, the data are sent to a shore-based center to be used for different purposes, including the following: feeding mathematical models describing stock evolution; identifying those regions with a large presence of individuals below a Minimum Conservation Reference Size (MCRS); and supporting administrative decisions about a given fishing region