The topology of artificial neural network ensure its powerful capacities such as association memory, self organization, auto adjustment, self learning and error tolerability. Properly trained ANN model possessed the flexibility as well as high accuracy. ANN, widely used as dynamic information system, was used in various fields such as pattern recognition, fitting,classification and decision making and prediction, so on. There are various issues related to above discussed fields in fisheries sciences. The principle and structure of ANN was discussed in this paper. Some examples such as applying BP (Error Back Propagation algorithm) and SOFM (Self Organization Feature Mapping) or Kohonen model to classification and pattern recognition, image process and identification, prediction and assessment, system simulation as well as optimization and multi objective decision were also introduced. Some restrictions or shortcomings of ANN modeling were also discussed according to the principle of modeling and data preprocessing. Furthermore, without cross verification, a network with a large number of weights and a modest amount of training data can overfit the training data, learning the noise present in the data rather than the underlying structure. The trend of ANN applications was put forward according to combination of fuzzy mathematics, mathematical logic, topology sciences as well as uncertainty.