What is the role of covariance matrix eigen decomposition in PCA?

Eigen decomposition identifies directions of maximum variance in the covariance matrix.

PCA computes covariance matrix of centered data and performs eigen decomposition to extract eigenvectors (principal directions) and eigenvalues (variance magnitude). Sorting eigenvalues in descending order gives principal components ranked by importance.