A new method is presented for identification of beta-barrel membrane proteins. 
It is based on a hidden Markov model (HMM) with an architecture obeying these 
proteins' construction principles. Once the HMM is trained, log-odds score 
relative to a null model is used to discriminate beta-barrel membrane proteins 
from other proteins. The method achieves only 10% false positive and false 
negative rates in a six-fold cross-validation procedure. The results compare 
favorably with existing methods. This method is proposed to be a valuable tool 
to quickly scan proteomes of entirely sequenced organisms for beta-barrel 
membrane proteins.