This dissertation is concerned with finite state machine-based technology for modeling natural language. Finite-state machines have proven to be efficient computational devices in modeling natural language phenomena in morphology and phonology. Because of their mathematical closure properties, finite-state machines can be manipulated and combined in many flexible ways that closely resemble formalisms used in different areas of linguistics to describe natural language. The use of finite-state transducers in constructing natural language parsers and generators has proven to be a versatile approach to describing phonological alternation, morphological constraints and morphotactics, and syntactic phenomena on the phrase level.The main contributions of this dissertation are the development of a new model of multitape automata, the development of a new logic formalism that can substitute for regular expressions in constructing complex automata, and adaptations of these techniques to solving classical construction problems relating to finite-state transducers, such as modeling reduplication and complex phonological replacement rules.The multitape model presented here goes hand-in-hand with the logic formalism, the latter being a necessary step to constructing the former. These multitape automata can then be used to create entire morphological and phonological grammars, and can also serve as a neutral intermediate tool to ease the construction of automata for other purposes.The construction of large-scale finite-state models for natural language grammars is a very delicate process. Making any solution practicable requires great care in the efficient implementation of low-level tasks such as converting regular expressions, logical statements, sets of constraints, and replacement rules to automata or finite transducers. To support the overall endeavor of showing the practicability of the logical and multitape extensions proposed in this thesis, a detailed treatment of efficient implementation of finite-state construction algorithms for natural language purposes is also presented.