Acoustical　signals　of　mechanical　systems　can　provide　original　information　of　operating　conditions,　and　thus　benefit　for　machinery　condition　monitoring　and　fault　diagnosis.　However,　acoustical　signals　measured　by　sensors　are　mixed　signals　of　all　the　sources,　and　normally　it　is　impossible　to　be　directly　used　for　acoustical　source　identification　or　feature　extraction.　Therefore,　this　paper　presents　nonlinear　factor　analysis　(NLFA)　and　applies　it　to　acoustical　source　separation　and　identification　of　mechanical　systems.　The　effects　by　numbers　of　hidden　neurons　and　mixed　signals　on　separation　performances　of　NLFA　are　comparatively　studied.　Furthermore,　acoustical　signals　from　a　test　bed　with　shell　structures　are　separated　and　identified　by　NLFA　and　correlation　analysis,　and　the　effectiveness　of　NLFA　on　acoustical　signals　is　validated　by　both　numerical　case　studies　and　an　experimental　case　study.　This　work　can　benefit　for　machinery　noise　monitoring,　reduction　and　control,　and　also　provide　pure　source　information　for　machinery　condition　monitoring　or　fault　diagnosis.