Abstract Over the past few years, the use of machine learning has gained considerable momentum in many industries, including exploration seismic. While supervised machine learning is increasingly being used in seismic data analysis, some obstacles hinder its widespread application. Seismic facies classification—a crucial aspect in this field—particularly faces challenges such as the selection of appropriate input attributes. Plethora of seismic attributes have been created over the years, and new ones are still coming out. Yet, several have been deemed redundant or geologically meaningless. In the context of machine learning, it is crucial to avoid these redundant and irrelevant attributes as they can result in overfitting, building unnecessary complex models, and prolonging computational time. The current study incorporates an attribute selection approach to seismic facies classification and evaluates the importance of several available seismic attributes. Two datasets from the AN Field and the Dangerous Grounds region offshore Malaysia were utilized. Several attribute selection techniques were evaluated, with most of them yielding perfect attribute subsets for the AN dataset. However, only the wrapper and embedded methods could produce optimal subsets for the more complex Dangerous Grounds dataset. In both datasets, distinguishing the targeted seismic facies was mainly dependent on amplitude, spectral, and gray-level co-occurrence matrix attributes. Furthermore, spectral magnitude components played a significant role in classifying the facies of the Dangerous Grounds broadband data. The study demonstrated the importance of attribute selection, established a workflow, and identified significant attributes that could enhance seismic facies classification in Malaysian basins and similar geologic settings.