Principal Component Analysis (PCA) has been employed for structural analysis of network traffic flows in order to capture the periodic, anomalous and noisy components of traffic flows. PCA suffers from fundamental limitation stemmed from the assumption that the variables in question are continuous random variables. Analysis of data for discrete random variables has been traditionally performed by employing Correspondence Analysis. In this work, we present a novel idea of structural analysis of network traffic flows using Correspondence Analysis (CA). Apart from overcoming several limitations of PCA, CA has an edge over PCA in terms of visualization of large traffic matrices.