The paper describes the automatic learning of parameters for
self-diagnosis of a system for automatic orientation of single
aerial images used by the State Survey Department of
Northrhine--Westfalia. The orientation is based on 3D lines as
ground control features, and uses a sequence of probabilistic
clustering, search and ML-estimation for robustly estimating the 6
parameters of the exterior orientation of an aerial image. The
system is interpreted as a classifier, making an internal
evaluation of its success. The classification is based on a number
of parameters possibly relevant for self-diagnosis. A hand
designed classifier reached 11% false negatives and 2% false
positives on appr. 17000 images. A first version of a new
classifier using support vector machines is evaluated. Based on
appr. 650 images the classifier reaches 2 % false negatives and 4%
false positives, indicating an increase in performance.