Developing　a　computational　method　for　recognizing　preterm　delivery　is　important　for　timely　diagnosis　and　treatment　of　preterm　delivery.　The　main　aim　of　this　study　was　to　evaluate　electrohysterogram　(EHG)　signals　recorded　at　different　gestational　weeks　for　recognizing　the　preterm　delivery　using　random　forest　(RF).　EHG　signals　from　300　pregnant　women　were　divided　into　two　groups　depending　on　when　the　signals　were　recorded:　i)　preterm　and　term　delivery　with　EHG　recorded　before　the　26th　week　of　gestation　(denoted　by　PE　and　TE　group),　and　ii)　preterm　and　term　delivery　with　EHG　recorded　during　or　after　the　26th　week　of　gestation　(denoted　by　PL　and　TL　group).　31　linear　features　and　nonlinear　features　were　derived　from　each　EHG　signal,　and　then　compared　comprehensively　within　PE　and　TE　group,　and　PL　and　TL　group.　After　employing　the　adaptive　synthetic　sampling　approach　and　six-fold　cross-validation,　the　accuracy　(ACC),　sensitivity,　specificity　and　area　under　the　curve　(AUC)　were　applied　to　evaluate　RF　classification.　For　PL　and　TL　group,　RF　achieved　the　ACC　of　0.93,　sensitivity　of　0.89,　specificity　of　0.97,　and　AUC　of　0.80.　Similarly,　their　corresponding　values　were　0.92,　0.88,　0.96　and　0.88　for　PE　and　TE　group,　indicating　that　RF　could　be　used　to　recognize　preterm　delivery　effectively　with　EHG　signals　recorded　before　the　26th　week　of　gestation.　(c)　2019　The　Author(s).　Published　by　Elsevier　B.V.　on　behalf　of　Nalecz　Institute　of　Biocybernetics　and　Biomedical　Engineering　of　the　Polish　Academy　of　Sciences.