DNA microarray is an efficient new technology that allows to analyze, at the same time, the expressionlevel of millions of genes. The gene expression level indicates the synthesis of different messenger ribonu-cleic acid (mRNA) molecule in a cell. Using this gene expression level, it is possible to diagnose diseases,identify tumors, select the best treatment to resist illness, detect mutations among other processes. Inorder to achieve that purpose, several computational techniques such as pattern classification approachescan be applied. The classification problem consists in identifying different classes or groups associatedwith a particular disease (e.g., various types of cancer, in terms of the gene expression level). However, theenormous quantity of genes and the few samples available, make difficult the processes of learning andrecognition of any classification technique. Artificial neural networks (ANN) are computational modelsin artificial intelligence used for classifying, predicting and approximating functions. Among the mostpopular ones, we could mention the multilayer perceptron (MLP), the radial basis function neural net-work (RBF) and support vector machine (SVM). The aim of this research is to propose a methodology forclassifying DNA microarray. The proposed method performs a feature selection process based on a swarmintelligence algorithm to find a subset of genes that best describe a disease. After that, different ANN aretrained using the subset of genes. Finally, four different datasets were used to validate the accuracy ofthe proposal and test the relevance of genes to correctly classify the samples of the disease.