This study evaluates the potential of Electronic Monitoring (EM) to monitor compliance in the Dutch bottom trawl fishery, which, eventually, can be used as a guideline for further preparation of an EM pilot in Dutch fisheries. Within this context the feasibility of EM to monitor compliance of the landing obligation (LO) in Dutch bottom trawl fisheries is analysed considering technical, practical and financial requirements for control purposes and the ability of EM to completely document the catch, including discards, as stated on under the LO. In addition, the social aspects around the implementation of EM are described: the negative perception of EM by many fishers and the options that facilitate its uptake by the fishing industry. A detailed review of the technical standards as described by the European Fisheries Control Agency (EFCA) and comparison with EM setups used during scientific trails points out that several technical standards can be considered as control specific. Technical applications on remote access, data back- up, data storage and transfer, camera positioning system diagnostics, drafting vessel monitoring plans and data exchange formats should be considered when implementing EM for LO control purposes. The estimated total budget needed for fleetwide implementation for EM on the current Dutch beam trawler fleet, approximately 105 vessels, is estimated at 1.2 million euro to set up an EM programme and 1.9 million euro for annual running costs. The efficiency of EM to register catch very much depends on the type of fishery and catch, e.g. species and catch composition. EM preforms better when catches are processed in such a manner that it is easy to detect individual fish on video footage. This depends on the type of fisheries, e.g. hook and line fisheries, or the monitoring objectives, i.e. specimens of a particular species, are easily spotted in the catch. Results of previous studies showed that EM is less efficient in detecting smaller specimens, e.g. undersized and discarded fish. Occlusions of fish and other organic material on the sorting belt were the main reason for structural underestimation of the discarded catch. Manual review of EM video data is perceived as a highly labour intensive and time consuming task. Implementation of protocols to increase visibility of discards improves efficiency of EM, but it comes with a cost. The estimated extra time needed to conduct a protocol to better display discarded catch in front of the cameras would exceed 12 hours per fishing trip. Computer vision technology will reduce time and manual labour currently needed for EM video review and decreases the level of dependency in discard recording of fishers for control purposes. In order to successfully implement EM, it is important that the fishing industry supports and accepts the tool as beneficial. Currently there is a sense of distrust and resistance on behalf of a large part of the fishing fleet. The incentives offered for fishers to participate in previous, more scientific orientated, EM trials were direct and consisted of individual quota uplifts, direct payments, increased days at sea, access to closed areas and increased flexibility in gear choice. However, in order to roll out EM over the entire Dutch (and European) fleet, a more intrinsic motivation is required. This can be fuelled by indirect incentives, such as increased market access though eco-labelling, but also by experiencing the advantages in terms of better fishing opportunities or healthy fisheries, because, EM provides better data for improved fisheries management. A first step in creating support is to involve the fishers in the process of planning and implementation. Managers, fishers, scientists, IT specialists and other relevant parties need to discuss the concerns of the parties involved, such as privacy, data security, data ownership, and information provision. The discussions are also an opportunity to manage expectations and to help build trust and confidence. The establishment of a multi-stakeholder group including industry representatives and experts could facilitate this process. Implementation of EM on a large scale requires a substantial investment. EM generates a significant amount of (video) data, and needs an infrastructure that supports data transfer, storage, and review facilities. Development of computer vision technology to support the EM implementation process is recommended, the technology reduces time, costs and manual review needed and enhances the recording of discards under the LO.