Astronomy has developed basically with the use of spectroscopic and imaging techniques, operated separately. With the development of modern technologies (such as IFUs) it is possible to obtain data cubes in which one combines both techniques simultaneously, producing images with spectral resolution. To extract information from them may become quite complex, and the development of new methods of data analysis is desirable. We present a method of analysis of data cubes (data from single field observations, containing two spatial and one spectral dimension) that uses PCA (Principal Component Analysis) to express the data in form of reduced dimensionality, facilitating the information extraction. PCA transforms the system of correlated coordinates into a system of uncorrelated coordinates ordered by principal components of decreasing variance. The new coordinates are called eigenvectors and the projections of the data on these coordinates produce images called tomograms. The association of the tomograms (images) to eigenvectors (eigenspectra) is important for the interpretation of both. The eigenvectors are orthogonal among themselves and this information is fundamental for their handling and interpretation. By handling eigenvectors and tomograms one can enhance features, extract noise, compress data, extract spectra etc. We applied the method to the central region of nearby galaxies like the LINERs NGC 4579 and NGC 4736. We show that NGC 4736 has a type 1 active nuclei, not known before. We also show that both AGN are displaced from the center of the stellar bulge of their host galaxies.