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In this paper, a new method based on Particle Swarm Optimization (PSO) for independent component analysis(ICA) is presented which can be applied for feature extraction. Due to the drawbacks of the Gradient method, it is replaced by PSO in Discriminant Independent Component Analysis (dICA ) algorithm in the proposed approach. The Gradient method may lead to local optimal and it cannot solve the problem of slow convergence since it includes a learning step which needs to be determined in advance. Moreover, Gradient-based techniques cannot achieve high level of accuracy because of the considerable complexity involved in ICA. The additional complexity of the Gradient-based algorithms leads to pseudo-optimal scenarios. The Discriminant Independent Component Analysis based on PSO is used to overcome these serious shortcomings. Most of the datasets used for simulation in this study are obtained from UCI repository. The results obtained using linear discriminant analysis (LDA), principal component analysis (PCA) and gradient-based dICA are compared with those obtained by PSO-dICA . The results show improvement in classification with PSO-dICA method compared to other methods. In other words, PSO-dICA method bought about classifier error reduction.