YouTube channels often reflect not just isolated video content but broader patterns in how creators structure and present their material over time. Despite growing interest in external engagement metrics and cross-modal signals, little attention has been paid to the internal consistency of content within a channel. This paper presents a multimodal framework for characterizing channels based on inner similarity across five key features, including titles, descriptions, transcripts, categories, and the video’s color palette. Using a dataset of 136 channels and 14,000 videos, we compute similarity scores and construct ten feature combinations to represent channels in a multidimensional consistency space. Five unsupervised clustering algorithms and majority voting are used to identify stable channel groupings. We discover three high-level characterizations that reflect recurring patterns of internal content alignment across semantic and visual features. The proposed framework is scalable, language-independent, and provides a novel lens for analyzing channel-level content behavior and editorial patterns.