Abstract Background Schizophrenia spectrum disorders are complex syndromes involving multiple clinical manifestations. Besides psychopathological symptoms, cognitive and motor alterations are also highly relevant in the context of the comprehension, assessment, and treatment of these disorders. Moreover, these three domains of clinical manifestations display complex reciprocal interactions that require further characterization. This work aims to use network analysis to investigate the associations between cognitive, motor, and psychopathological alterations in schizophrenia spectrum disorders. This approach might prove to be advantageous in identifying key variables for the assessment and treatment of these disorders. Methods A sample of 732 patients with schizophrenia spectrum disorders from a multi-site cohort study was included in the analysis. We estimated a network using a regularized Gaussian Graphical Model and conducted network stability analyses. Twenty-six nodes were included, encompassing items from the Positive and Negative Syndrome Scale, multiple neuropsychological tests, and clinician-assessed extrapyramidal symptoms’ scores. The results were further explored with centrality analyses and network comparisons between subgroups defined according to illness duration and remission status. Results We found that the estimated network was densely interconnected. Furthermore, nodes representing symptoms of disorganization were very central and, therefore, pivotal in connecting other psychopathological symptoms to cognitive and motor alterations. The estimated network for the subgroup of patients in remission showed a more sparse density and a different structure from the network of non-remitted patients. Discussion In conclusion, in the context of a broader representation of schizophrenia spectrum disorders’ manifestations, our results of a network analysis confirm a close association between different symptom domains and unveil a highly influential role of disorganization symptoms. Moreover, structural differences in networks occur according to remission status. These results are relevant for research in nosology, clinical assessment, and treatment approaches.