In modern industrial environments, quality control is imperative for ensuring product reliability and operational efficiency. Despite advancements in automatic data collection and data-driven models for quality processes, certain critical variables still require manual entry due to the branch of activity from which they originate, leading to potential errors from human involvement. This paper focuses on developing and applying two methodologies, Soft Sensors (SS) and Principal Component Analysis (PCA), to detect data entry errors in quality control variables. The developed methodologies were tested across three industrial datasets and the following error types were studied: blank spaces, duplicates, measurement errors, order errors, and extra number errors.