Process connectivity is a key information that is sought in a diverse set of applications ranging from design to fault diagnosis of engineering and biological processes. The present work develops a methodology for reconstruction of plant connectivity from dynamic data using directional spectral analysis, a novel adaptation of ideas from neurosciences and econometrics. The method is based on the concept of Granger causality while the procedure rests on the directional decomposition of power spectrum into direct and indirect energy transfers. The quantification of effective connectivity is obtained using a structural vector auto-regressive (SVAR) representation of the process. Results from simulation studies demonstrate the potential of the proposed method. © 2012 IFAC.