mhurdle is a package for R enabling the estimation of a wide set of regression models where the dependent variable is left censored at zero, which is typically the case in household expenditure surveys. These models are of particular interest to explain the presence of a large proportion of zero observations for the dependent variable by means of up to three censoring mechanisms, called hurdles. For the analysis of censored household expenditure data, these hurdles express a good selection mechanism, a desired consumption mechanism and a purchasing mechanism, respectively. However, the practical scope of these paradigmatic hurdles is not restricted to empirical demand analysis, as they have been fruitfully used in other fields of economics, including labor economics and contingent valuation. For each these censoring mechanisms, a continuous latent variable is defined, indicating that censoring is in effect when the latent variable is negative. Latent variables are modeled as the sum of a linear function of explanatory variables and of a normal random disturbance with a possible correlation between the disturbances of different latent variables. To model possible departures of the observed dependent variable to normality, we use flexible transformations allowing rescaling skewed or leptokurtic random variables to heteroscedastic normality. mhurdle models are estimated using the maximum likelihood method for random samples. Model evaluation and selection are tackled by means of goodness of fit measures and Vuong tests. Real-world illustrations of the estimation of multiple hurdles models are provided using data from consumer expenditure surveys.