  A very important issue concerning protostellar jets is the mechanism behind
their formation. Obtaining information on the region at the base of a jet can
shed light into the subject and some years ago this has been done through a
search for a rotational signature at the jet line spectrum. The existence of
such signatures, however, remains controversial. In order to contribute to the
clarification of this issue, in this paper we show that the Principal Component
Analysis (PCA) can potentially help to distinguish between rotation and
precession effects in protostellar jet images. We apply the PCA to synthetic
spectro-imaging datacubes generated as an output of numerical simulations of
protostellar jets. In this way we generate a benchmark to which a PCA
diagnostics of real observations can be confronted. Using the computed emission
line profiles for [O I]6300A and [S II]6716A, we recover and analyze the
effects of rotation and precession in tomograms generated by PCA. We show that
different combinations of the eigenvectors can be used to enhance and to
identify the rotation features present in the data. Our results indicate that
the PCA can be useful for disentangling rotation from precession in jets with
an inclination of the jet with respect to the plane of the sky as high as 45
degrees. We have been able to recover the initially imposed rotation jet
profile for models at moderate inclination angle (< 15 degrees) and without
precession (abridged).
