Background.　Uterine　contraction　(UC)　is　the　tightening　and　shortening　of　the　uterine　muscles　which　can　indicate　the　progress　of　pregnancy　towards　delivery.　Electrohysterogram　(EHG),　which　reflects　uterine　electrical　activities,　has　recently　been　studied　for　UC　monitoring.　In　this　paper,　we　aimed　to　evaluate　different　EHG　segments　for　recognizing　UCs　using　the　convolutional　neural　network　(CNN).　Materials　and　Methods.　In　the　open-access　Icelandic　16-electrode　EHG　database　(122　recordings　from　45　pregnant　women),　7136　UC　and　7136　non-UC　EHG　segments　with　the　duration　of　60　s　were　manually　extracted　from　107　recordings　of　40　pregnant　women　to　develop　a　CNN　model.　A　fivefold　cross-validation　was　applied　to　evaluate　the　CNN　based　on　sensitivity　(SE),　specificity　(SP),　and　accuracy　(ACC).　Then,　1056　UC　and　1056　non-UC　EHG　segments　were　extracted　from　the　other　15　recordings　of　5　pregnant　women.　Furthermore,　the　developed　CNN　model　was　applied　to　identify　UCs　using　different　EHG　segments　with　the　durations　of　10　s,　20　s,　and　30　s.　Results.　The　CNN　achieved　the　average　SE,　SP,　and　ACC　of　0.82,　0.93,　and　0.88　for　a　60　s　EHG　segment.　The　EHG　segments　of　10　s,　20　s,　and　30　s　around　the　TOCO　peak　achieved　higher　SE　and　ACC　than　the　other　segments　with　the　same　duration.　The　values　of　SE　from　20　s　EHG　segments　around　the　TOCO　peak　were　higher　than　those　from　10　s　to　30　s　EHG　segments　on　the　same　side　of　the　TOCO　peak.　Conclusion.　The　proposed　method　could　be　used　to　determine　the　efficient　EHG　segments　for　recognizing　UC　with　the　CNN.