  The evaluation of coherent noise can provide useful information in the study
of detectors. The identification of coherent noise sources is also relevant for
uncertainty calculations in analyse where several channels are combined. The
study of the covariance matrix give information about coherent noises. Since
covariance matrix of high dimension data could be difficult to analyse, the
development of analysis tools is needed. Principal Component Analysis (PCA) is
a powerful tool for such analysis. It has been shown that we can use PCA to
find coherent noises in ATLAS calorimeter or the CALICE Si-W electromagnetic
calorimeter physics prototype. However, if several coherent noise sources are
combined, the interpretation of the PCA may become complicated.
  In this paper, we present another method based on the study of the covariance
matrix to identify noise sources. This method has been developed for the study
of front end ASICs dedicated to CALICE calorimeters. These calorimeters are
designed and studied for experiments at the ILC. We also study the reliability
of the method with simulations. Although this method has been developped for a
specific application, it can be used for any multi channel analysis.
