  A crucial step in planet hunting surveys is to select the best candidates for
follow up observations, given limited telescope resources. This is often
performed by human `eyeballing', a time consuming and statistically awkward
process. Here we present a new, fast machine learning technique to separate
true planet signals from astrophysical false positives. We use Self Organising
Maps (SOMs) to study the transit shapes of \emph{Kepler} and \emph{K2} known
and candidate planets. We find that SOMs are capable of distinguishing known
planets from known false positives with a success rate of 87.0\%, using the
transit shape alone. Furthermore, they do not require any candidates to be
dispositioned prior to use, meaning that they can be used early in a mission's
lifetime. A method for classifying candidates using a SOM is developed, and
applied to previously unclassified members of the \emph{Kepler} KOI list as
well as candidates from the \emph{K2} mission. The method is extremely fast,
taking minutes to run the entire KOI list on a typical laptop. We make
\texttt{Python} code for performing classifications publicly available, using
either new SOMs or those created in this work. The SOM technique represents a
novel method for ranking planetary candidate lists, and can be used both alone
or as part of a larger autovetting code.
