AbstractWe introduce a mixture of generalized hyperbolic distributions as an alternative to the ubiquitous mixture of Gaussian distributions as well as their near relatives within which the mixture of multivariatet‐distributions and the mixture of skew‐tdistributions predominate. The mathematical development of our mixture of generalized hyperbolic distributions model relies on its relationship with the generalized inverse Gaussian distribution. The latter is reviewed before our mixture models are presented along with details of the aforesaid reliance. Parameter estimation is outlined within the expectation–maximization framework before the clustering performance of our mixture models is illustrated via applications on simulated and real data. In particular, the ability of our models to recover parameters for data from underlying Gaussian and skew‐tdistributions is demonstrated. Finally, the role of generalized hyperbolic mixtures within the wider model‐based clustering, classification, and density estimation literature is discussed.The Canadian Journal of Statistics43: 176–198; 2015 © 2015 Statistical Society of Canada