Training　of　the　convolution　neural　network　(CNN)　is　a　problem　of　global　optimisation.　This　study　proposed　a　hybrid　modified　particle　swarm　optimisation　(MPSO)　and　conjugate　gradient　(CG)　algorithm　for　efficient　training　of　CNN.　The　training　involves　MPSO-CG　to　avoid　trapping　in　local　minima.　Particularly,　improvements　in　the　MPSO　by　introducing　a　novel　approach　for　control　parameters,　improved　parameters　updating　criteria,　a　novel　parameter　in　the　velocity　update　equation,　and　fusion　of　the　CG　allows　handling　the　issues　in　training　CNN.　In　this　study,　the　authors　validate　the　proposed　MPSO　algorithm　on　three　benchmark　mathematical　test　functions　and　also　compared　with　three　different　variants　of　the　baseline　particle　swarm　optimisation　algorithm.　Furthermore,　the　performance　of　the　proposed　MPSO-CG　is　also　compared　with　other　training　algorithms　focusing　on　the　analysis　of　computational　cost,　convergence,　and　accuracy　based　on　a　standard　problem　specific　to　classification　applications　on　CIFAR-10　dataset　and　face　and　skin　detection　dataset.　©　2020　Institution　of　Engineering　and　Technology.　All　rights　reserved.