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We presents in this paper a novel fish classification methodology based on acombination between robust feature selection, image segmentation andgeometrical parameter techniques using Artificial Neural Network and DecisionTree. Unlike existing works for fish classification, which propose descriptorsand do not analyze their individual impacts in the whole classification taskand do not make the combination between the feature selection, imagesegmentation and geometrical parameter, we propose a general set of featuresextraction using robust feature selection, image segmentation and geometricalparameter and their correspondent weights that should be used as a prioriinformation by the classifier. In this sense, instead of studying techniquesfor improving the classifiers structure itself, we consider it as a black boxand focus our research in the determination of which input information mustbring a robust fish discrimination.The main contribution of this paper isenhancement recognize and classify fishes based on digital image and To developand implement a novel fish recognition prototype using global featureextraction, image segmentation and geometrical parameters, it have the abilityto Categorize the given fish into its cluster and Categorize the clustered fishinto poison or non-poison fish, and categorizes the non-poison fish into itsfamily .

Fish recognition based on the combination between robust feature  selection, image segmentation and geometrical parameter techniques using  Artificial Neural Network and Decision Tree