  One way to recognise an object is to study how the echo has been shaped
during the interaction with the target. Wideband sonar allows the study of the
energy distribution for a large range of frequencies. The frequency
distribution contains information about an object, including its inner
structure. This information is a key for automatic recognition. The scattering
by a target can be quantitatively described by its Form Function. The Form
Function can be calculated based on the data of the initial pulse, reflected
pulse and parameters of a medium where the pulse is propagating. In this work
spherical objects are classified based on their filler material - water or air.
We limit the study to spherical 2 layered targets immersed in water. The Form
Function is used as a descriptor and fed into a Neural Network classifier,
Multilayer Perceptron (MLP). The performance of the classifier is compared with
Support Vector Machine (SVM) and the Form Function descriptor is examined in
contrast to the Time and Frequency Representation of the echo.
