Maximum smoothed likelihood estimation can improve maximum likelihood estimation by focusing on larger features of the likelihood function. This approach is useful when numerical techniques struggle with small-scale details. The study introduces a class of maximum smoothed likelihood estimators that work well for models with local asymptotic normality. In two examples, these estimators showed good properties for smooth but multimodal likelihoods and non-smooth likelihoods.