A　regression　model　with　skew-normal　errors　provides　a　useful　extension　for　ordinary　normal　regression　models　when　the　dataset　under　consideration　involves　asymmetric　outcomes.　In　this　article,　we　explore　the　use　of　Markov　Chain　Monte　Carlo　(MCMC)　methods　to　develop　a　Bayesian　analysis　for　joint　location　and　scale　nonlinear　models　with　skew-normal　errors,　which　relax　the　normality　assumption　and　include　the　normal　one　as　a　special　case.　The　main　advantage　of　these　class　of　distributions　is　that　they　have　a　nice　hierarchical　representation　that　allows　the　implementation　of　MCMC　methods　to　simulate　samples　from　the　joint　posterior　distribution.　Finally,　simulation　studies　and　a　real　example　are　used　to　illustrate　the　proposed　methodology.