Part 1 Introduction: Faster computing platforms; Powerful software; Better connectivity. Part 2 Guide to some computerized artificial intelligence methods: What is artificial intelligence?; Knowledge-based systems (expert systems); Neural networks; Genetic algorithms; Simulated annealing; Some future directions for artificial intelligence. Part 3 Potential for geographical information systems (GIS) in fisheries management: Value of spatial analyses; Data sources for a fisheries GIS; Present uses of marine GIS; Some potential uses of GIS in marine fisheries science; Ways of introducing a fisheries GIS. Part 4 Quantitative fisheries research surveys, with special reference to computers: Egg and larval surveys; Mark-recapture experiments; Trawl surveys; Acoustic surveys; Integrated surveys; Auxiliary instruments; Potential survey applications of new or unique acoustic instruments or techniques. Part 5 Geostatistics and their applications to fisheries survey data: Structural analyses - the variogram; Use of the variogram - estimation of variance; Use of the variogram - kriging; More advanced structural models than the variogram; Practical example. Part 6 Pattern recognition: Image preprocessing; Identifying phytoplankton; Identifying fish stocks from otolith shape differences; Age determination and growth-rate measurements of fish; Identifying species from echo sounders; Needed developments; Problems and promises. Part 7 Computers in fisheries population dynamics: The beginnings; Fitting models to data using electronic computers; Analysis of management policies. Part 8 Multispecies modelling of fish populations: Multispecies modelling in fisheries; Individual-based approach; Multispecies example; Summary and future directions. Part 9 Computers and the future of fisheries: Opening new windows for measuring fishery dynamics; Designing robust policies for living with uncertainty; Keeping the bad guys at bay - seeing how fisheries really operate; Exploring options and opportunities - integrating assessment and management; Pitfalls - mega-models, mega-information, and mega-mistakes.