The aim of this study was to investigate the effectiveness of Empirical Mode Decomposition (EMD) for slow wave extraction from multichannel electrogastrographical signal (EGG) the cutaneous recording of gastric myoelectrical activity. From the pacemaker region of stomach both spontaneous depolarization and repolarization occur generating the myoelectrical waves that are called the gastric pacesetter potentials, or slow waves. The 3 cycles per minute (3pcm) (0.05Hz) slow wave is fundamental electrical phenomenon in stomach responsible for the propagation and maximum frequency of stomach contractions. Appropriate spread of gastric contractions is a key for the correct stomach emptying whereas delay in this action causes various gastric disorders, such as bloating, vomiting or unexplained nausea. Unfortunately the EGG signal is not a pure one but usually a sort of mixture consisting of respiratory signals, cardiac signals, random noise and possible myoelectrical activity from other organs surrounding the stomach, such as duodenum or small intestine. Identify and removal of contaminations from different artifactual sources from the EGG recording is a major task before EGG analysis and interpretation. The use of EMD method and Hilbert spectrum combination for slow wave extraction from raw EGG signal seems to be a good choice, because this adaptive decomposition technique is unique suitable for both nolinear, no-stationary data analysis.