A Parallel PCA Neural Network Approach for Feature Extraction ABSTRACT Principal Component Analysis (PCA) is a well known statistical method that has successfully been applied for reducing data dimensionality. Focusing on a neural network which approximates the results obtained by classical PCA, the main contribution of this work consists in introducing a parallel modeling for such network. A comparative study shows that the proposal presents promising results when a multi-core computer is available.