For people who cannot see non-textual web content, such as images, maps or audio files, the alternative texts are crucial to understand and use the content. Alternate texts are often automatically generated by web publishing software or not properly provided by the author of the content. Such texts may impose web accessibility barriers. Automatic accessibility checkers in use today can only detect the presence of alternative texts, but not determine if the text is describing the corresponding content in any useful way. This paper presents a pattern recognition approach for automatic detection of alternative texts that may impose a barrier, reaching an accuracy of more then 90%.