This paper proposes a neural network recognition system for hand keying Radio Morse codes. The system has been trained and tested on real world data recorded from amateur radio Morse codes. The overall recognizing process can be partitioned into 3 major parts, the preprocessing, the feature extracting, and the character decoding. The whole operation is able to be performed in real-time on a PC/486 system. Self-Organizing Maps are used intensively in the recognition system to adaptively learn the variation of the Morse code signal. The average performance of the recognition system has been achieved about 96.4% with a rejection rate of 6.5%. It is hoped that many of the techniques would be applicable to a wide range of DSP and recognition tasks.