A text parser was used to calculate the statistical distribution of words within an email body. This information was used by a neuro-fuzzy system to determine the spam classification of the email. This process of detecting spam in an email was experimentally found to be 90% efficient. This design is exceptionally good as compared to present day filters based on its simplicity and limited scope of detection methods. Our system could be further improved by incorporating other identifiers of email spam.