  In this paper, a recently developed numerical technique [{\em Tuncer E and
Guba{\'n}ski S M, IEEE Trans Diel El Insul {\bf 8}(3)(2001) 310-320}] is
applied to poly(propylene glycol) complex dielectric data to extract more
information about the molecular relaxation processes. The method is based on a
constrained-least-squares (\clsq) data fitting procedure together with the
Monte Carlo (\mc) method. We preselect the number of relaxation times with no
{\em a-priori} physical assumption, and use the Debye single relaxation as
``kernel'', then the obtained weighting factors at each \mc step from the \clsq
method builds up a relaxation time spectrum. When the analysis is repeated for
data at different temperatures a {\em relaxation-image} is created. The
obtained relaxation are analyzed using the Lorentz (Cauchy) distribution, which
is a special form of the L{\'e}vy statistics. In the present report the $\beta$
and $\alpha$ relaxations are resolved for the \ppg. A comparison of the
relaxations to those earlier reported in the literature indicate that the
presented method provides additional information compared to methods based on
empirical formulas. The distribution of relaxation times analysis is especially
useful to probe the crossover region where the $\alpha$- and $\beta$-
relaxations merge and the results show that the relaxation after the crossover
region at higher temperatures is Arrhenius-type as the $\beta$-relaxation.
Moreover, this relaxation is more likely to be the continuation of the
$\beta$-relaxation, but with a different activation energy.
