As　the　representative　of　electrical　activity　from　uterine　muscle,　electrohysterogram　(EHG)　is　recorded　non-invasively　by　multiple　electrodes　positioned　on　the　abdominal　surface.　The　purpose　of　our　paper　is　to　estimate　different　electrode　configurations　for　recognizing　uterine　contractions　(UCs)　with　EHG　signals.　8-electrode　configuration　was　taken　as　an　example　to　shove　our　novel　method　with　convolutional　neural　network　(CNN)　classification　and　score.　The　open　accessed　Icelandic　16-electrode　EHG　database　was　adopted　in　our　study.　With　8-electrode　configuration,　EHG　signals　corresponding　to　UCs　and　non-UCs　were　segmented　and　saved　as　image　patches.　The　CNN　was　established　and　trained　by　thousands　of　EHG　segments.　The　performance　of　CNN　was　evaluated　by　the　area　under　curve　(AUC)　and　accuracy　of　recognizing　UCs　and　non-UCs.　Seven　different　8-electrode　configurations　were　scored　and　ranked.　It　was　found　the　8-electrode　configuration　with　4　on　the　uterine　fundus,　2　on　the　both　and　2　on　the　cervix　achieved　the　AUC　of　0.766　and　the　highest　score　of　2.197.　Among　the　configurations　we　have　tried,　it　is　concluded　that　the　8　electrodes　in　4-2-2　configuration　placed　along　the　uterus　as　an　upside-down　pear　could　provide　the　most　important　information　for　recognition　of　UC　based　on　our　experiments.