Particles are not equally and systematically categorized in traditional Croatian grammars, as there is no single precise definition for this category that all authors could use as a guide with no room for different interpretations. Since the status of a lexeme as a particle is mainly determined by its semantic function in a sentence, and since language production by its nature contains a high level of linguistic creativity, as well as considering the fact that the number of sentence constructions and combinations of individual lexemes within them is immeasurable, traditional grammars as a rule include a number of other types of speech, such as adverbs, in the category of articles depending on their function in a particular context. This is especially the case when considering the semantic aspect of each utterance, either in terms of intended meaning or in terms of semantic relations between lexemes. However, such categorization of particles is neither practical nor productive when it comes to computer processing, in this case specifically part-of-speech tagging for Croatian language corpora, because in this kind of processing the semantic and pragmatic nuances of language use are mostly lost and the focus is on the forms and syntactic roles of lexemes. The aim of this paper is to limit the list of Croatian particles to the smallest possible number of invariable words that can undoubtedly be considered particles regardless of context. Such an approach seems to be the most productive solution for part-of-speech tagging due to its simplicity and the fact that the remaining lexemes that are traditionally doubly categorized as particles, despite primarily belonging to another part of speech, can still be easily categorized according to their primary categories without the interference of semantic criteria that are not available to computers at the same level as to humans. At the same time, this approach avoids the issue of varying semantic interpretations in a type of language processing that primarily requires clear distinctions and precision.