  We consider spatially homogeneous marked point patterns in an unboundedly
expanding convex sampling window. Our main objective is to identify the
distribution of the typical mark by constructing an asymptotic
$\chi^2$-goodness-of-fit test. The corresponding test statistic is based on a
natural empirical version of the Palm mark distribution and a smoothed
covariance estimator which turns out to be mean square consistent. Our approach
does not require independent marks and allows dependences between the mark
field and the point pattern. Instead we impose a suitable $\beta$-mixing
condition on the underlying stationary marked point process which can be
checked for a number of Poisson-based models and, in particular, in the case of
geostatistical marking. In order to study test performance, our test approach
is applied to detect anisotropy of specific Boolean models.
