  JUNO is a multi-purpose neutrino experiment currently under construction in
Jiangmen, China. It is primary aiming to determine the neutrino mass ordering.
Moreover, its 20\,kt target mass makes it an ideal detector to study neutrinos
from various sources, including nuclear reactors, the Earth and its atmosphere,
the Sun, and even supernovae. Due to the small cross section of neutrino
interactions, the event rate of neutrino experiments is limited. In order to
maximize the signal-to-noise ratio, it is extremely important to control the
background levels. In this paper we discuss the potential of particle
identification in JUNO, its underlying principles and possible areas of
application in the experiment. While the presented concepts can be transferred
to any large liquid scintillator detector, our methods are evaluated
specifically for JUNO and the results are mainly driven by its high optical
photon yield of 1,200 photo electrons per MeV of deposited energy. In order to
investigate the potential of event discrimination, several event pairings are
analysed, i.e. $\alpha/\beta$, $p/\beta$, $e^+/e^-$, and $e^-/\gamma$. We
compare the discrimination performance of advanced analytical techniques based
on neural networks and on the topological event reconstruction keeping the
standard Gatti filter as a reference. We use the Monte Carlo samples generated
in the physically motivated energy intervals. We study the dependence of our
cuts on energy, radial position, PMT time resolution, and dark noise. The
results show an excellent performance for $\alpha/\beta$ and $p/\beta$ with the
Gatti method and the neural network. Furthermore, $e^+/e^-$ and $e^-/\gamma$
can partly be distinguished by means of neural network and topological
reconstruction on a statistical basis. Especially in the latter case, the
topological method proved very successful.
