  In this report we present experimental results using
\emph{Haussdorf-Besicovich} fractal dimension for performing morphological
galaxy classification. The fractal dimension is a topological, structural and
spatial property that give us information about the space were an object lives.
We have calculated the fractal dimension value of the main types of galaxies:
ellipticals, spirals and irregulars; and we use it as a feature for classifying
them. Also, we have performed an image analysis process in order to standardize
the galaxy images, and we have used principal component analysis to obtain the
main attributes in the images. Galaxy classification was performed using
machine learning algorithms: C4.5, k-nearest neighbors, random forest and
support vector machines. Preliminary experimental results using 10-fold
cross-validation show that fractal dimension helps to improve classification,
with over 88 per cent accuracy for elliptical galaxies, 100 per cent accuracy
for spiral galaxies and over 40 per cent for irregular galaxies.
