Multiconlitron　is　a　general　framework　for　constructing　piecewise　linear　classifiers.　For　the　convexly　separable　and　the　commonly　separable　datasets,　it　can　correctly　separate　them　by　using　support　conlitron　algorithm　(SCA)　and　multiconlition　algorithm　(SMA),　respectively.　On　this　basis,　a　soft　margin　method　for　multiconlitron　design　is　proposed.　Firstly,　the　training　samples　are　mapped　from　the　input　space　to　a　high　dimensional　feature　space,　and　one　class　of　those　samples　is　clustered　into　some　groups　by　K-means　algorithm.　Then,　the　conlitron　is　constructed　between　each　group　and　another　class　of　samples,　and　the　integrated　model,　multiconlitron,　is　obtained.　The　proposed　method　can　overcome　the　inapplicability　of　the　original　model　to　commonly　inseparable　datasets.　By　simplifying　the　model　structure,　the　proposed　method　further　improves　the　classification　accuracy　and　the　generalization　ability.　Experimental　results　show　that　the　proposed　method　achieves　better　performance　compared　with　some　other　piecewise　linear　classifiers　and　its　effectiveness　and　advantages　are　verified.