Abstract: A decision tree classifier was developed for sea bottom recognition from acoustic echoes. The acoustic data was acquired by DT4000 echosounder at 200 kHz frequency. The performance of the classifying system was investigated involving various backscattered echo parameters, in particular wavelet coefficients. The results of the decision tree classification were compared with those obtained from the adaptive neuro-fuzzy system (IFNN) involving reduced number of input parameters by the use of Principal Component Analysis (PCA).