When　a　sigmoidal　feedforward　neural　network　(SFNN)　is　trained　by　the　gradient-based　algorithms,　the　quality　of　the　overall　learning　process　strongly　depends　on　the　initial　weights.　To　improve　the　algorithm　stability　and　avoid　local　minima,　a　Mutual　Information　based　weight　initialization　(MIWI)　method　is　proposed　for　SFNN.　The　useful　information　contained　in　input　variables　is　measured　with　the　mutual　information　(MI)　between　input　variables　and　output　variables.　The　initial　distribution　of　weights　is　consistent　with　the　information　distribution　in　the　input　variables.　The　lower　and　upper　bounds　of　the　weights　range　are　calculated　to　ensure　the　neurons　inputs　are　within　the　active　region　of　sigmoid　function.　The　MIWI　method　makes　the　initial　weights　close　to　the　global　optimal　point　with　a　higher　probability　and　avoids　premature　saturation.　The　efficiency　of　the　MIWI　method　is　evaluated　based　on　several　benchmark　problems.　The　experimental　results　show　that　the　stability　and　accuracy　of　the　proposed　method　are　better　than　some　other　weight　initialization　methods.　(C)　2016　Elsevier　B.V.　All　rights　reserved.