Based on simple methods such as observing word and part of speech tag co-occurrence and clustering, we generate syntactic parses of sentences in an entirely unsupervised and self-inducing manner. The parser learns the structure of the language in question based on measuring “breaking points” within sentences. The learning process is divided into two phases, learning and application of learned knowledge. The basic learning works in an iterative manner which results in a hierarchical constituent representation of the sentence. Part-of-Speech tags are used to circumvent the data sparseness problem for rare words. The algorithm is applied on untagged data, on manually assigned tags and on tags produced by an unsupervised part of speech tagger. The results are unsurpassed by any self-induced parser and challenge the quality of trained parsers with respect to finding certain structures such as noun phrases.