The Mixture of Latent Trait Analyzers (MLTA) represents a model-based clustering approach specifically tailored to multivariate categorical data. It accommodates clustering of units through a finite mixture specification, while also accounting for the residual latent variability of units within each cluster through a set of multidimensional latent variables (traits). The original formulation is extended to account for the effect of concomitant variables (covariates). These are allowed to affect cluster formation, the conditional outcome distribution, both (as in standard mixtures of experts models), or neither. Overall, the proposal improves the model’s flexibility and its capacity to reflect the complexity of the data.