Online adaptation is a powerful means to handle unexpected slow or catastrophic
changes of the system's behavior (e.g., a stuck or broken rudder of an aircraft).
Therefore, adaptation is one way for realizing a self-healing system.
Substantial research and development has been made to use neural networks (NN)
for such tasks (e.g., integrated in various unmanned helicopters and te
st-flown on a modified F-15 aircraft).
Despite the advantages of adaptive neural network based systems, the lack of
methods to perform certification, verification, and validation (V\&V) of such
systems severely restricts their applicability.
In this paper, we report on ongoing work to develop V\&V techniques and
processes for NN-based safety-critical control systems, in our case
an aircraft flight control system.
Although the project ultimately aims at V\&V of online adaptive systems,
this paper focuses on the first part of this project dealing with so-called
pre-trained neural networks (PTNN). V\&V techniques developed here are important
pre-requisites for handling the online adaptive case. In particular, we
describe highlights of a process guide which has been developed within this
project and discuss important V\&V issues which need to be addressed during
certification.