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The traditional estimation of mixture regression models is based on the
assumption of normality (symmetry) of component errors and thus is sensitive to out-
liers, heavy-tailed errors and/or asymmetric errors. In this work we present a proposal
to deal with these issues simultaneously in the context of the mixture regression by
extending the classic normal model by assuming that the random errors follow a scale
mixtures of skew-normal distributions. This approach allows us to model data with
great flexibility, accommodating skewness and heavy tails. The main virtue of consid-
ering the mixture regression models under the class of scale mixtures of skew-normal
distributions is that they have a nice hierarchical representation which allows easy
implementation of inference. We develop a simple EM-type algorithm to perform
maximum likelihood inference of the parameters of the proposed model. In order to
examine the robust aspect of this flexible model against outlying observations, some