This　paper　aimed　to　evaluate　the　effect　of　various　electrode　configurations　on　applying　a　convolutional　neural　network　(CNN)　to　recognize　uterine　contraction　(UC)　with　Electrohysterogram　(EHG)　signals.　Seven　8-electrode　configurations　and　thirteen　4-electrode　configurations　were　selected　from　the　4　x　4　electrode　grid　in　the　Icelandic　16-electrode　EHG　database.　EHG　signals　were　divided　into　UC　and　non-UC　sections　of　45　seconds　and　saved　as　images.　Each　8-electrode　configuration　with　7152　images　and　4-electrode　configuration　with　3576　images　were　applied　to　train　CNN　to　recognize　UCs.　A　scoring　method　was　proposed　based　on　the　area　under　the　curve　(AUC)　and　the　accuracy　to　evaluate　the　effect　of　electrode　configurations　on　recognizing　UCs.　The　EHG　signals　from　the　4　electrodes　on　the　upper　left　of　the　uterus　showed　the　best　classification　performance　(AUC　=　0.79,　Accuracy　=　0.72,　Score　=　2.30).