Uterine　contraction　(UC)　activity　is　commonly　used　to　monitor　the　approach　of　labour　and　delivery.　Electrohysterograms　(EliGs)　have　recently　been　used　to　monitor　UC　and　distinguish　between　efficient　and　inefficient　contractions.　In　this　study,　we　aimed　to　identify　UC　in　EHG　signals　using　a　convolutional　neural　network　(CNN).　An　open-access　database　(Icelandic　16-electrode　EHG　database　from　45　pregnant　women　with　122　recordings,　DB1)　was　used　to　develop　a　CNN　model,　and　14000　segments　with　a　length　of　45　s　(7000　from　UCs　and　7000　from　non-UCs,　which　were　determined　with　reference　to　the　simultaneously　recorded　tocography　signals)　were　manually　extracted　from　the　122　EHG　recordings.　Five-fold　cross-validation　was　applied　to　evaluate　the　ability　of　the　CNN　to　identify　UC　based　on　its　sensitivity　(SE),　specificity　(SP),　accuracy　(ACC),　and　area　under　the　receiver　operating　characteristic　curve　(AUC).　The　CNN　model　developed　using　DB1　was　then　applied　to　an　independent　clinical　database　(DB2)　to　further　test　its　generalisation　for　recognizing　UCs.　The　EHG　signals　in　DB2　were　recorded　from　20　pregnant　women　using　our　multi-channel　system,　and　308　segments　(154　from　UCs　and　154　from　non-UCs)　were　extracted.　The　CNN　model　from　five-fold　cross-validation　achieved　average　SE,　SP,　ACC,　and　AUC　of　0.87,　0.98,　0.93,　and　0.92　for　DB1,　and　0.88,　0.97,　0.93,　and　0.87　for　DB2,　respectively.　In　summary,　we　demonstrated　that　CNN　could　effectively　identify　UCs　using　EHG　signals　and　could　be　used　as　a　tool　for　monitoring　maternal　and　foetal　health.