Automated identification and  classification  of  short-duration
oceanic  signals obtained from passive sonar is a complex problem
because of the large variability in both  temporal  and  spectral
characteristics  even  in  signals obtained from the same source.
This paper presents the design and evaluation of a  comprehensive
classifier  system  for such signals.  We first highlight the im-
portance of selecting appropriate signal descriptors  or  feature
vectors  for  high-quality  classification  of  realistic  short-
duration oceanic signals.  Wavelet-based feature  extractors  are
shown  to  be  superior  to the more commonly used autoregressive
coefficients and power spectral coefficients for this purpose.  A
variety  of  static  neural network classifiers are evaluated and
compared favorably with traditional  statistical  techniques  for
signal classification.  We concentrate on those networks that are
able to tune out irrelevant input features and are less suscepti-
ble  to  noisy inputs, and introduce two new neural-network based
classifiers.  Methods for combining the outputs of several  clas-
sifiers  to  yield  a  more  accurate  labeling are  proposed and
evaluated based on the interpretation of  network outputs as  ap-
proximating posterior class probabilities.  These methods lead to
higher classification accuracy and also provide a  mechanism  for
recognizing   deviant  signals  and  false  alarms.   Performance
results are given for signals in the DARPA standard data set I.