  Powerful telescopes equipped with multi-fibre or integral field spectrographs
combined with detailed models of stellar atmospheres and automated fitting
techniques allow for the analysis of large number of stars. These datasets
contain a wealth of information that require new analysis techniques to bridge
the gap between observations and stellar evolution models. To that end, we
develop BONNSAI (BONN Stellar Astrophysics Interface), a Bayesian statistical
method, that is capable of comparing all available observables simultaneously
to stellar models while taking observed uncertainties and prior knowledge such
as initial mass functions and distributions of stellar rotational velocities
into account. BONNSAI can be used to (1) determine probability distributions of
fundamental stellar parameters such as initial masses and stellar ages from
complex datasets, (2) predict stellar parameters that were not yet
observationally determined and (3) test stellar models to further advance our
understanding of stellar evolution. An important aspect of BONNSAI is that it
singles out stars that cannot be reproduced by stellar models through
$\chi^{2}$ hypothesis tests and posterior predictive checks. BONNSAI can be
used with any set of stellar models and currently supports massive
main-sequence single star models of Milky Way and Large and Small Magellanic
Cloud composition. We apply our new method to mock stars to demonstrate its
functionality and capabilities. In a first application, we use BONNSAI to test
the stellar models of Brott et al. (2011a) by comparing the stellar ages
inferred for the primary and secondary stars of eclipsing Milky Way binaries.
Ages are determined from dynamical masses and radii that are known to better
than 3%. We find that the stellar models reproduce the Milky Way binaries well.
BONNSAI is available through a web-interface at
http://www.astro.uni-bonn.de/stars/bonnsai.
