This paper presents simultaneous inference procedures for general parametric models, where the experimental questions are specified through linear combinations of elemental model parameters. The framework extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression, generalized linear models, linear mixed effects models, the Cox model, robust linear models, and others. The procedures adjust for multiplicity and control the overall type I error rate. The paper describes the general model and derives asymptotic or exact distributions of linear functions of elemental parameters. It then outlines a framework for simultaneous inference procedures, including global and simultaneous inference methods. The paper also discusses applications in multiple linear regression, one-way ANOVA, and other parametric models. The R package multcomp is used to implement these procedures, providing a convenient interface for simultaneous inference. The paper includes numerical examples and illustrates the application of simultaneous inference in various contexts, such as variable selection in linear regression models, comparisons in ANOVA models, and survival analysis. The methods are shown to be applicable to a wide range of statistical models, including generalized linear models, mixed models, survival models, and robust models. The paper concludes that these procedures can be applied to almost all classical and modern statistical models, enabling honest decisions based on simultaneous inference while maintaining a pre-specified familywise error rate.This paper presents simultaneous inference procedures for general parametric models, where the experimental questions are specified through linear combinations of elemental model parameters. The framework extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression, generalized linear models, linear mixed effects models, the Cox model, robust linear models, and others. The procedures adjust for multiplicity and control the overall type I error rate. The paper describes the general model and derives asymptotic or exact distributions of linear functions of elemental parameters. It then outlines a framework for simultaneous inference procedures, including global and simultaneous inference methods. The paper also discusses applications in multiple linear regression, one-way ANOVA, and other parametric models. The R package multcomp is used to implement these procedures, providing a convenient interface for simultaneous inference. The paper includes numerical examples and illustrates the application of simultaneous inference in various contexts, such as variable selection in linear regression models, comparisons in ANOVA models, and survival analysis. The methods are shown to be applicable to a wide range of statistical models, including generalized linear models, mixed models, survival models, and robust models. The paper concludes that these procedures can be applied to almost all classical and modern statistical models, enabling honest decisions based on simultaneous inference while maintaining a pre-specified familywise error rate.